Unmanned Ground Vehicle Two-Level Planning 

Technology Assessment 

by Barry A. Bodt, Marshal A. Childers, Susan G. Hill, Richard S. Camden, 

Juan Pablo Gonzalez, Robert M. Dean, William F. Dodson, Greg Kreafle, 

Alberto LaCaze, and Leonard Sapronov 

ARL-TR-5331 September 2010 

Approved for public release; distribution is unlimited. 
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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5067 

ARL-TR-5331 September 2010 

Unmanned Ground Vehicle Two-Level Planning 

Technology Assessment 

Barry A. Bodt 

Computational and Information Sciences Directorate, ARL 

Marshal A. Childers

Vehicle Technology Directorate, ARL 

Susan G. Hill 

Human Research and Engineering Directorate, ARL 

Richard S. Camden 

L3-MPRI 

Juan Pablo Gonzalez, Robert M. Dean, William F. Dodson, and Greg Kreafle 

General Dynamic Robotics Systems 

Alberto LaCaze and Leonard Sapronov 

Robotic Research, LLC

Approved for public release; distribution is unlimited.
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4. TITLE AND SUBTITLE 

Unmanned Ground Vehicle Two-Level Planning Technology Assessment 

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6. AUTHOR(S) 

Barry A. Bodt, Marshal A. Childers,* Susan G. Hill,† Richard S. Camden,‡ Juan 

Pablo Gonzalez,§ Robert M. Dean,§ William F. Dodson,§ Greg Kreafle,§ Alberto 

LaCaze,** and Leonard Sapronov**

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0602618A 

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U.S. Army Research Laboratory 

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Aberdeen Proving Ground, MD 21005-5067 

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Vehicle Technology Directorate, Aberdeen Proving Ground, MD 21005 

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Human Research and Engineering Directorate, Aberdeen Proving Ground, MD 21005 

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L3-MPRI, Bldg. 1121, Aberdeen Proving Ground, MD 21005 

§

General Dynamics Robotic Systems, 1231 Tech Ct., Westminster, MD 21157 

**Robotic Research, LLC, 555 Quince Orchard Rd., Ste. 300, Gaithersburg, MD 20878 

14. ABSTRACT 

The recently concluded 8-year Robotics Collaborative Technology Alliance (RCTA) pursued technologies to support tactical 

behaviors of unmanned ground systems as a way to illustrate advances in the research program thrust areas of perception, 

intelligent control, and Soldier-machine interface. In April 2009, the U.S. Army Research Laboratory conducted an experiment 

at Fort Indiantown Gap, PA, to measure the capability of current autonomous navigation planning algorithms to solve complex 

terrain challenges previously not achievable under the local planning approach used in Demo III, the forerunner program and 

baseline for the RCTA. The principal focus was to evaluate the relative performance of three planning algorithms representing 

the span of research from Demo III to the closing months of the RCTA. 

15. SUBJECT TERMS

unmanned ground vehicle, UGV, two-level planning, robotics 

16. SECURITY CLASSIFICATION OF: 

17. LIMITATION 

OF ABSTRACT 

UU 

18. NUMBER 

OF PAGES 

122 

19a. NAME OF RESPONSIBLE PERSON

Barry A. Bodt 

a. REPORT 

Unclassified

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

19b. TELEPHONE NUMBER (Include area code) 

410-278-6659 

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18 
iii 

Contents 

List of Figures vii

List of Tables xi 

Acknowledgments xii 

1. Introduction 1

2. Background 2

2.1 History .............................................................................................................................2

2.2 Purpose of the Experiment ..............................................................................................3

2.3 Three Autonomous Navigation Approaches ...................................................................3

2.3.1 Autonomous Mobility .........................................................................................4

2.3.2 Dynamic Replanner .............................................................................................4

2.3.3 Field Cost Interface .............................................................................................6

3. Robotic Interactive Visualization and Exploitation Technology (RIVET) 7

3.1 Representing Mobility Features ......................................................................................8

3.2 Path Blockages ................................................................................................................8

4. Scenario Background 9

5. Experimental Approach 10

5.1 Experimental Design .....................................................................................................10

5.2 Parametric Study in Simulation .....................................................................................10

5.3 Field Experiment ...........................................................................................................11

5.4 Data Collected ...............................................................................................................11

5.5 Data Reduction ..............................................................................................................12

5.6 Graphics for Each Run ..................................................................................................12

6. Scenario 1 13

6.1 Situation.........................................................................................................................13

6.2 Expectations ..................................................................................................................13
iv 

6.3 Results ...........................................................................................................................14 

6.4 Autonomous Mobility ...................................................................................................15

6.5 Dynamic Replanner .......................................................................................................15

6.6 Field Cost Interface .......................................................................................................15

7. Scenario 2 16

7.1 Situation.........................................................................................................................16

7.2 Expectations ..................................................................................................................16

7.3 Results ...........................................................................................................................17

7.4 Autonomous Mobility ...................................................................................................17

7.5 Dynamic Replanner .......................................................................................................17

7.6 Field Cost Interface .......................................................................................................18

8. Scenario 3 18

8.1 Situation.........................................................................................................................18

8.2 Expectations ..................................................................................................................19

8.3 Results ...........................................................................................................................19

8.4 Autonomous Mobility ...................................................................................................20

8.5 Dynamic Replanner .......................................................................................................20

8.6 Field Cost Interface .......................................................................................................21

9. Scenario 4 21

9.1 Situation.........................................................................................................................21

9.2 Expectations ..................................................................................................................22

9.3 Results ...........................................................................................................................22

9.4 Autonomous Mobility ...................................................................................................22

9.5 Dynamic Replanner .......................................................................................................22

9.6 Field Cost Interface .......................................................................................................22

10. Scenario 5 23

10.1 Situation.........................................................................................................................23

10.2 Expectations ..................................................................................................................23

10.3 Results ...........................................................................................................................24

10.4 Autonomous Mobility ...................................................................................................24
v 

10.5 Dynamic Replanner .......................................................................................................24

10.6 Field Cost Interface .......................................................................................................25

11. Scenario 6 25

11.1 Situation.........................................................................................................................25

11.2 Expectations ..................................................................................................................26

11.3 Results ...........................................................................................................................26

11.4 Autonomous Mobility ...................................................................................................26

11.5 Dynamic Replanner .......................................................................................................26

11.6 Field Cost Interface .......................................................................................................27

12. Scenario 7 27

12.1 Situation.........................................................................................................................27

12.2 Expectations ..................................................................................................................28

12.3 Results ...........................................................................................................................28

12.4 Autonomous Mobility ...................................................................................................28

12.5 Dynamic Replanner .......................................................................................................29

12.6 Field Cost Interface .......................................................................................................29

13. Discussion and Analysis 29

13.1 Analytical Approach......................................................................................................29

13.2 Expectations in RIVET .................................................................................................30

13.3 Expectations in the Field ...............................................................................................31

13.3.1 Reaching the Goal Point ....................................................................................31

13.3.2 AM Observations ..............................................................................................34

13.3.3 DR Observations ...............................................................................................34

13.3.4 FCI Observations ...............................................................................................34

13.4 Scenario Difficulty ........................................................................................................34

14. Excursion Scenario 1.1 35

14.1 Situation.........................................................................................................................35

14.2 Dynamic Replanner .......................................................................................................36

15. Excursion Scenario 6.1 36

15.1 Situation.........................................................................................................................36
vi 

15.2 Expectations ..................................................................................................................37

15.3 Results ...........................................................................................................................37

15.4 Autonomous Mobility ...................................................................................................37

15.5 Dynamic Replanner .......................................................................................................37

15.6 Field Cost Interface .......................................................................................................38

16. Excursion Scenario 6.2 38

16.1 Situation.........................................................................................................................38

16.2 Expectations ..................................................................................................................38

16.3 Results ...........................................................................................................................38

16.4 Field Cost Interface .......................................................................................................38

17. Effects of Varying CellScale 39

18. Similarities and Differences Between RIVET and Field Runs 39

18.1 RIVET ...........................................................................................................................39

18.2 Matched Runs ................................................................................................................40

18.3 Lack of Variability in RIVET .......................................................................................41

18.4 E-stops ...........................................................................................................................42

18.5 HMP ..............................................................................................................................42

18.6 Backups .........................................................................................................................42

18.7 Performance of FCI in Scenarios 5 and 6......................................................................43

18.8 Reasons for End of Mission ..........................................................................................44

19. Implications for Operator Performance 44

20. Conclusions 45

Appendix A. Graphics and Narrative of All Runs 49

Appendix B. Experimenter’s Observation of All Runs 99

List of Symbols, Abbreviations, and Acronyms 107

Distribution List 108
vii 

List of Figures 

Figure 1. An initial route planned by the global planner. ...............................................................4

Figure 2. An alternate route generated by the DR. .........................................................................5

Figure 3. Second alternative route generated by the DR. ...............................................................5

Figure 4. HMP-generated maneuver. ..............................................................................................6

Figure 5. Field costs at cul-de-sac in scenario 5. ............................................................................7

Figure 6. Scenario 5 cul-de-sac with blocked trails, as seen in RIVET. .........................................8

Figure 7. Scenario 5 cul-de-sac with blocked trails. .......................................................................9

Figure 8. Sample graphic from run 76. .........................................................................................12

Figure 9. Scenario 1 description. ..................................................................................................13

Figure 10. Scenario 2 description. ................................................................................................16

Figure 11. Scenario 3 description. ................................................................................................19

Figure 12. RIVET run 290. ...........................................................................................................20

Figure 13. Scenario 4 description. ................................................................................................21

Figure 14. Scenario 5 description. ................................................................................................23

Figure 15. Scenario 6 description. ................................................................................................25

Figure 16. Scenario 7 description. ................................................................................................27

Figure 17. Run 7 where XUV solves cul-de-sac but does not achieve the goal point. .................32

Figure 18. Comparison of completed runs vs. solved runs. ..........................................................33

Figure 19. Scenario difficulty (percent goal achieved DR and FCI only). ...................................35

Figure 20. Scenario 1.1 description. .............................................................................................35

Figure 21. Scenario 1.1 runs with no NBC blockage. ..................................................................36

Figure 22. Scenario 6.1 description. .............................................................................................37

Figure 23. Example of closely matching RIVET and field runs. ..................................................40

Figure 24. Examples of lack of variability in RIVET. ..................................................................41

Figure 25. Time spent in HMP. ....................................................................................................42

Figure 26. Number of XUV backups. ...........................................................................................43 

Figure A-1. Run 238-AM-RIVET. ...............................................................................................49

Figure A-2. Run 33-AM-Field. .....................................................................................................49

Figure A-3. Run 231-DR-RIVET. ................................................................................................50

Figure A-4. Run 31.1-DR-Field. ...................................................................................................51

Figure A-5. Run 42-DR-Field. ......................................................................................................51
viii 

Figure A-6. Run 35-DR-Field. ......................................................................................................52

Figure A-7. Run 38-DR-Field .......................................................................................................52

Figure A-8. Run 43-DR-Field. ......................................................................................................53

Figure A-9. Run 226-FCI-RIVET. ................................................................................................53

Figure A-10. Run 34-FCI-Field. ...................................................................................................54

Figure A-11. Run 39-FCI-Field. ...................................................................................................54

Figure A-12. Run 32-FCI-Field. ...................................................................................................55

Figure A-13. Run 241-AM-RIVET. .............................................................................................55

Figure A-14. Run 47-AM-Field. ...................................................................................................56

Figure A-15. Run 51-AM-Field. ...................................................................................................56

Figure A-16. Run 52-AM-Field. ...................................................................................................57

Figure A-17. Run 57-AM-Field. ...................................................................................................57

Figure A-18. Run 206-DR-RIVET. ..............................................................................................58

Figure A-19. Run 48-DR-Field. ....................................................................................................58

Figure A-20. Run 49-DR-Field. ....................................................................................................59

Figure A-21. Run 53-DR-Field. ....................................................................................................59

Figure A-22. Run 56-DR-Field. ....................................................................................................60

Figure A-23. Run 220-FCI-RIVET. ..............................................................................................60

Figure A-24. Run 50-FCI-Field. ...................................................................................................61

Figure A-25. Run 54-FCI-Field. ...................................................................................................61

Figure A-26. Run 46.2-FCI-Field. ................................................................................................62

Figure A-27. Run 55.1-FCI-Field. ................................................................................................62

Figure A-28. Run 293-AM-RIVET. .............................................................................................63

Figure A-29. Run 63-AM-Field. ...................................................................................................63

Figure A-30. Run 64-AM-Field. ...................................................................................................64

Figure A-31. Run 62-DR-Field. ....................................................................................................64

Figure A-32. Run 68-DR-Field .....................................................................................................65

Figure A-33. Run 72-DR-Field. ....................................................................................................65

Figure A-34. Run 65-DR-Field. ....................................................................................................66

Figure A-35. Run 286-FCI-RIVET. ..............................................................................................66

Figure A-36. Run 61-FCI-Field. ...................................................................................................67

Figure A-37. Run 69-FCI-Field. ...................................................................................................67

Figure A-38. Run 66-FCI-Field. ...................................................................................................68

Figure A-39. Run 283-AM-RIVET. .............................................................................................68
ix 

Figure A-40. Run 280-DR-RIVET. ..............................................................................................69

Figure A-41. Run 275-FCI-RIVET. ..............................................................................................69

Figure A-42. Run 234-AM-RIVET. .............................................................................................70

Figure A-43. Run 16.1-AM-Field. ................................................................................................70

Figure A-44. Run 20-AM-Field. ...................................................................................................71

Figure A-45. Run 23-DR-RIVET. ................................................................................................71

Figure A-46. Run 30-DR-Field. ....................................................................................................72

Figure A-47. Run 27-DR-Field. ....................................................................................................72

Figure A-48. Run 22-DR-Field. ....................................................................................................73

Figure A-49. Run 19-DR-Field. ....................................................................................................73

Figure A-50. Run 18-DR-Field. ....................................................................................................74

Figure A-51. Run 12-FCI-RIVET. ................................................................................................74

Figure A-52. Run 23-FCI-Field. ...................................................................................................75

Figure A-53. Run 28-FCI-Field. ...................................................................................................75

Figure A-54. Run 17-FCI-Field. ...................................................................................................76

Figure A-55. Run 25.2-FCI-Field. ................................................................................................76

Figure A-56. Run 21.1-FCI-Field. ................................................................................................77

Figure A-57. Run 261-AM-RIVET. .............................................................................................77

Figure A-58. Run 78-AM-Field. ...................................................................................................78

Figure A-59. Run 258-DR-RIVET. ..............................................................................................78

Figure A-60. Run 77.1-DR-Field. .................................................................................................79

Figure A-61. Run 89-DR-Field. ....................................................................................................79

Figure A-62. Run 86-DR-Field. ....................................................................................................80

Figure A-63. Run 84-DR-Field. ....................................................................................................80

Figure A-64. Run 80-DR-Field. ....................................................................................................81

Figure A-65. Run 251-FCI-RIVET. ..............................................................................................81

Figure A-66. Run 88-FCI-Field. ...................................................................................................82

Figure A-67. Run 85-FCI-Field. ...................................................................................................82

Figure A-68. Run 82-FCI-Field. ...................................................................................................83

Figure A-69. Run 79.1-FCI-RIVET..............................................................................................83

Figure A-70. Run 76-FCI-Field. ...................................................................................................84

Figure A-71. Run 248-AM-RIVET. .............................................................................................84

Figure A-72. Run 8-AM-Field. .....................................................................................................85

Figure A-73. Run 4-AM-Field. .....................................................................................................85
x 

Figure A-74. Run 2-AM-Field. .....................................................................................................86

Figure A-75. Run 10-AM-Field. ...................................................................................................86

Figure A-76. Run 13-AM-Field. ...................................................................................................87

Figure A-77. Run 212-DR-RIVET. ..............................................................................................87

Figure A-78. Run 1-DR-Field. ......................................................................................................88

Figure A-79. Run 6-DR-Field. ......................................................................................................88

Figure A-80. Run 7-DR-Field. ......................................................................................................89

Figure A-81. Run 11-DR-Field. ....................................................................................................89

Figure A-82. Run 15-DR-Field. ....................................................................................................90

Figure A-83. Run 224-FCI-RIVET. ..............................................................................................90

Figure A-84. Run 14-FCI-Field. ...................................................................................................91

Figure A-85. Run 9-FCI-Field. .....................................................................................................91

Figure A-86. Run 5-FCI-Field. .....................................................................................................92

Figure A-87. Run 3-FCI-Field. .....................................................................................................92

Figure A-88. Run 12-FCI-Field. ...................................................................................................93

Figure A-89. Scenario 1.1 description. .........................................................................................93

Figure A-90. Scenario 1.1 runs with no NBC blockage. ..............................................................94

Figure A-91. Run 113.9-AM-Field. ..............................................................................................95

Figure A-92. Run 108.9-AM-Field. ..............................................................................................95

Figure A-93. Run 106.9-DR-Field. ...............................................................................................96

Figure A-94. Run 111.9-DR-Field. ...............................................................................................96

Figure A-95. Run 112.9-FCI-Field. ..............................................................................................97

Figure A-96. Run 107.9-FCI-Field. ..............................................................................................97

Figure A-97. Run 110.9-FCI-Field. ..............................................................................................98

Figure A-98. Run 109.9-FCI-Field. ..............................................................................................98
xi 

List of Tables 

Table 1. The number of runs executed in RIVET and in the field. ...............................................11

Table 2. Scenario 1 results. ...........................................................................................................14

Table 3. Scenario 2 results. ...........................................................................................................17

Table 4. Scenario 3 results. ...........................................................................................................19

Table 5. Scenario 4 results. ...........................................................................................................22

Table 6. Scenario 5 results. ...........................................................................................................24

Table 7. Scenario 6 results. ...........................................................................................................26

Table 8. Scenario 7 results. ...........................................................................................................28

Table 9. RIVET runs achieving goal point. ..................................................................................31

Table 10. Field runs reaching the goal point.................................................................................31

Table 11. Field runs solving the cul-de-sac. .................................................................................33

Table 12. Scenario 6.1 results. ......................................................................................................37

Table 13. Scenario 6.2 results. ......................................................................................................38

Table 14. Number of circles for FCI runs in scenarios 5 and 6. ...................................................39

Table 15. Correlations between RIVET and field results. ............................................................40

Table 16. Number of FCI circles in scenarios 5 and 6 using RIVET. ..........................................43

Table 17. Reasons for early end of mission calls. .........................................................................44

Table 18. Goal achieved/runs attempted. ......................................................................................46

Table 19. Challenges solved/challenges attempted. .....................................................................46
xii 

Acknowledgments 

We thank Jason Owens of the U.S. Army Research Laboratory for his help in producing the 

experimental unmanned vehicle path images that appear in this report. We also thank the 

technicians of General Dynamics Robotic Systems for their support during the preparation and 

performance of the technology assessment.
1 

1. Introduction 

The recently concluded 8-year Robotics Collaborative Technology Alliance (RCTA) pursued 

technologies to support tactical behaviors of unmanned ground systems as a way to illustrate 

advances in the research program thrust areas of perception, intelligent control, and Soldiermachine interface. In April 2009, the U.S. Army Research Laboratory (ARL) conducted an 

experiment at Fort Indiantown Gap (FTIG), PA, to measure the capability of current autonomous 

navigation planning algorithms to solve complex terrain challenges previously not achievable 

under the local planning approach used in Demo III, the forerunner program and baseline for the 

RCTA. The principal focus was to evaluate the relative performance of three planning 

algorithms representing the span of research from Demo III to the closing months of the RCTA. 

The approaches evaluated were the (1) Demo III Autonomous Mobility (AM) Planner with local 

planning only, (2) Dynamic Replanner (DR) with shared sensed data and a continuous update of 

the global plan, and (3) Field Cost Interface (FCI) Planner, which continuously generated a 

combined cost (local + global) by combining the kinematic constraints of the vehicle and the 

recommendations of the global planner. The Dynamic and FCI planners involve two-level 

autonomous mobility planning to solve route challenges, adding to the local planner established 

in Demo III the ability to update the global map with sensed data and to revise the route with 

both local and global information considered. An additional High-Maneuverability Planner 

(HMP) was available to DR for local repositioning to make feasible the execution of dynamically 

determined routes, hence calling for a change in direction outside the kinematic constraints of the 

platform. 

Prior to the field experiment, the experimental conditions were modeled in robotic interactive 

visualization and exploitation technology (RIVET), a high-fidelity hardware-in-the-loop

simulator, and the experimental design was executed in simulation. Simulating the experiment 

served to (1) confirm the feasibility and level of challenge for the proposed route, and (2) 

establish a reference set for the expected performance of the planners evaluated. The design 

included seven route scenarios of varying difficulty and three planners. Three replications of a 

full factorial design (63 runs) were run in RIVET. Each scenario had an achievable goal point 

(based on natural terrain alone), but each route was physically blocked to create a cul-de-sac 

impassable by the experimental unmanned vehicle (XUV) under the initial plan. Thus, in order 

for a planner to successfully navigate the XUV to the goal, an alternative route had to be found, 

first out of the cul-de-sac and then to the goal. 

In the field experimentation at FTIG, at least two replications of each scenario were attempted 

for each planner. The AM planner reached the goal on 33% of the runs, FCI achieved 48% of 

the goal points, and DR achieved 64% of the goal points. A distinction in performance was 
2 

made between achieving the goal point and solving the intermediate cul-de-sac challenge. The 

AM planner solved 40% of the cul-de-sacs, FCI solved 52%, and DR solved 79%. This is a 

major improvement in maneuvering in complex terrain and is a technology enabler for more 

complex tactical behaviors and reduced operator workload. 

RIVET was successful in predicting about half (48%) of the field run performance, but the lack 

of run-to-run variability in RIVET hindered its usefulness in building a reference distribution of 

performance. Its use as a planning tool, however, was invaluable. The feasibility and level of 

challenge confirmed in simulation translated to the field extremely well, so that differences in 

performance anticipated among the three planners were observed in the field. Setting an 

appropriate level of challenge for an experiment had been elusive in earlier studies without 

RIVET, resulting in some inefficient testing. 

2. Background 

2.1 History 

In 2000, the ARL Demo III unmanned ground vehicle (UGV) technology program demonstrated 

integrated UGV perception and planning for autonomous navigation in unstructured 

environments. The purpose of Demo III was to develop UGV technology that enabled robust 

autonomous mobility in relevant environments. The focus for UGV performance measurement 

was twofold: (1) to determine the level of maturity of autonomous mobility technology and (2) 

to measure the impact on Soldier workload when operator intervention is required to continue 

UGV operation. In 2002 and 2003, the Demo III program conducted a series of experiments at 

three sites that ascertained the technology readiness level (TRL) of autonomous navigation for 

UGVs.1

 The UGV platform was the XUV. With regard to mobility, the accomplishment in 

Demo III was the enhanced local navigation around obstacles as the XUV followed a 

predetermined waypoint route up to 2 km long. Successful performance in a relevant 

environment earned a TRL-6 designation for the autonomous navigation system at that time. 

Since 2003, technology developments under the ARL RCTA have been measured in terms of 

their contribution to notional mission profiles envisioned by the Future Combat Systems 

Program.1

 Generally, two tracks of experimentation have been followed: (1) tactical behaviors, 

especially as to conducting reconnaissance and (2) safe operations when the UGV is operating in 

close proximity to humans.1

 1

Camden, R.; Bodt, B.; Schipani, S.; Bornstein, J.; Runyon, T.; French, F.; Shoemaker, C.; Jacoff, A.; Lytle, A. Autonomous 

Mobility Technology Assessment Final Report; ARL-TR-3471; U. S. Army Research Laboratory: Aberdeen Proving Ground, 

MD, 2005. 
3 

2.2 Purpose of the Experiment 

The purpose of this experiment is to measure the advances in autonomous navigation under 

extremely challenging scenarios. In the TRL-6 study, navigation was stressed primarily by 

vegetation and natural obstacle challenges, impacting the way toward the next waypoint of a 

predetermined route. In this study, the challenge was more austere. Planning algorithms were 

used to guide the XUV between waypoints and around natural obstacles and, to be more flexible 

in completing a mission, to untether the robot from a predetermined waypoint route to move 

tactically to a goal point. This autonomous tactical movement required the planner to resolve 

incomplete or erroneous terrain information or unexpected terrain situations, making the original 

planned path impossible to traverse. 

To accomplish tactical movement, the methods for resolving local challenges had to evolve. 

Since the time of Demo III and the TRL-6 experiment, when faced with a situation where no 

traversable local path could be found by the XUV, the method to finding an alternate path has 

been to successively back up (5, 10, and 15 m) and search for an acceptable local path to the next 

waypoint through the terrain ahead using a sensor-based near-field (~40 m) representation of the 

environment. This approach emphasizes the view in front of the XUV and assumes that the 

planned path is achievable if a small tolerance is given to the XUV to deviate from that planned 

path around any local obstacle. This approach (local planning only) had a limited capability 

because it required frequent operator assistance in order to continue to the goal point. Locally 

sensed data remained at the local level. Higher-level planning relied upon static, a priori data 

from an external source and resulted in a predetermined waypoint route. 

Research on two-level (local and global) planning was a major initiative in the RCTA. Sensed 

terrain data would be shared from the local to the global level, and routes would potentially be 

adjusted in light of the new information. It was believed that new methods for finding 

alternative paths in consideration of a goal point would result. The autonomy of the vehicle 

would be enhanced, and the operator’s workload would be reduced. 

No operator interventions (teleop, manual backup, etc.) were permitted in this experiment to 

keep the focus on the technologies designed to improve autonomous mobility. 

2.3 Three Autonomous Navigation Approaches 

In recent years, researchers from the RCTA have developed several new approaches to 

combining local and global planning into a hierarchical approach to autonomous navigation.2,3

These approaches have the potential to enable the XUV to overcome complex terrain challenges 

and significantly reduce the need for operator assistance in successfully completing a tactical 

 2Childers, M.; Bodt, B.; Hill, S.; Camden, R. The Impact of Multi-Level Path Planning on Unmanned Ground Vehicle 

Tactical Behavior; 26th Army Science Conference, Orlando, FL, December 2008. 

3

Childers, M.; Bodt, B.; Hill, S.; Camden, R.; Dean, R.; Dodson, W.; Sutton, L.; Sapronov, L. Unmanned Ground Vehicle 

Tactical Behaviors Technology Assessment; ARL-TR-4698: U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, 

2009. 
4 

movement. The ultimate goal for the development of such planning algorithms is to achieve 

adaptive control in dynamic, unfamiliar situations. The experiment focused on measuring the 

performance differences among three planning approaches and assessing the benefit they provide 

to the UGV when faced with challenging situations. 

2.3.1 Autonomous Mobility 

In this mode, the XUV receives an initial global route generated by the Command and Control 

global planner from a priori data. The XUV then uses the perception system and the local AM 

planner to follow this route and avoid obstacles along the way. The local planner considers the 

kinematic constraints of the XUV but has no knowledge of conditions beyond the sensor range 

(~40 m). In this mode, there are no updates to the global route, and the XUV will most likely 

fail to find an alternative if there are blockages that invalidate the original route and that cannot 

be avoided with local maneuvers. 

Figure 1 shows the initial plan generated from a priori data from the start point (SP) to the goal. 

The AM planner would attempt to follow this route and would normally do so successfully if the 

a priori data is reasonably accurate. 

Figure 1. An initial route planned by the global planner. 

2.3.2 Dynamic Replanner 

The DR makes use of sensed terrain data passed from local to global maps (called best 

information planning). The global map is now continuously improved with current, locally 

sensed terrain information. In this mode, the global planner generates an initial optimal route 

based on the a priori feature data. As the global map is updated, a new global route is 
5 

continuously generated and compared to the current route. When a sufficient difference in the 

routes occurs, the new global route is sent to the XUV. The global plan always ignores the 

kinematic constraints of the XUV, so if the route has an initial heading that differs from the 

vehicle’s current heading by more than 45°, the HMP is called to reposition the vehicle before 

following the intended route. 

Figure 2 illustrates a new global plan generated by the DR after sensing the blockage on the 

original planned route, and figure 3 illustrates a new global plan at the point where it sensed that 

the first alternative route was also blocked. 

Figure 2. An alternate route generated by the DR. 

Figure 3. Second alternative route generated by the DR.
6 

The HMP is a search-based, nonholonomic planner that models not only the position (x,y) of the 

XUV, but also its heading and its limited turning radius. As such, it is able to provide paths that 

are kinematically correct and that are not limited to a predetermined set of egographs. The 

planner has a small set of primitive motions that are repeated over the search space, generating 

complex trajectories that adapt to the constraints of the environment. This planner is able to 

automatically generate maneuvers, such as three-point turns and “k” turns. However, because of 

the complexity of its planning space, in a few seconds HMP can only plan trajectories within a 

small region near the vehicle (~20 m). Figure 4 illustrates an HMP-planned maneuver when the 

dynamic replanner sends a new plan that begins more than 45° from the current trajectory. 

Figure 4. HMP-generated maneuver. 

2.3.3 Field Cost Interface 

The FCI planner continuously generates a cost field at a radius R from the vehicle, using both 

prior data and sensed data. FCI then generates a combined cost (local + global) at each point in 

the circle defined by the radius R, thereby combining the kinematic constraints of the vehicle and 

the recommendations of the global planner. The lowest combined cost becomes the new local 

plan. Figure 5 illustrates the field costs at the “donut” surrounding the XUV as it moves toward 

the first blockage in scenario 5. 
7 

Figure 5. Field costs at cul-de-sac in scenario 5. 

The FCI’s performance depends on the weight that global costs are given when combined with 

local costs: 

 Ctotal = C’local + CellScale*C’global , (1) 

where C’local and C’global are scaled local and global path costs. Prior to this assessment, the value 

of CellScale was tuned using RIVET simulations and field experiments in the fall of 2008. 

Based on a limited number of runs, a value of 0.2 was determined to perform best and was the 

value chosen for this assessment. Unlike with the DR, the HMP has not been integrated with the 

FCI planner, putting FCI at a disadvantage where the ability to maneuver in a tight space is 

required. 

3. Robotic Interactive Visualization and Exploitation Technology (RIVET) 

RIVET is a high-fidelity hardware-in-the-loop simulator that not only generates a rich world 

representation in real time, but also simulates sensors and the interactions of the vehicle with the 

terrain and other moving entities, while having the same low-level interface as the actual 

hardware in the robotic vehicle. 

Developed under the RCTA, RIVET features a game-based, interactive, multiuser modeling and 

simulation platform specifically suited to developing perception, planning, and other robotic 

technologies faster and more affordably. RIVET’s feature-rich environment and scalability 

allow users to configure the system to include pedestrians as well as air-, ground-, and sea-based 

robotic assets so that realistic small-team tasks can be played out safely and repetitively in the 

virtual world. 
8 

By leveraging the latest developments in game technology and graphics hardware, RIVET is able 

to provide stunning graphics, special effects, high-fidelity physics simulation, terrain rendering, 

sensor simulation, and weather effects, such as fog and rain. 

3.1 Representing Mobility Features 

The current representation used in RIVET allows limited modeling of local terrain characteristics 

that excludes ruts, shoulders, gravel, etc. As a result, subsystems that detect and use these 

features are not well simulated. In the assessment, this was evident in some of the more rugged 

runs in which the behavior of the vehicle in the field exceeded behaviors in RIVET. 

3.2 Path Blockages 

In the synthetic environment of RIVET, XUV routes were blocked by inserting barrel entities 

into the simulation environment at the desired location across the width of the trail. This 

provided an effective path blockage, as illustrated in figure 6. 

Figure 6. Scenario 5 cul-de-sac with blocked trails, as seen in RIVET.
9 

In the field, creating a blocked path for the XUV was considerably more difficult. Vehicles, 

barrels, and cones were needed to fully block the path along the trails. Any area of sparse 

shrubs/trees was also augmented with man-made obstacles to achieve the intended blockage 

through an area. 

Figure 7 is a panoramic photo of the actual area simulated in RIVET, where the trail on the left is 

blocked with barrels and cones, the low brush in the middle is blocked with cones, and the trail 

on the right is blocked with a high-mobility multipurpose wheeled vehicle (HMMWV). One can 

easily see the difference (compare to figure 6) between RIVET and the actual area in the 

representation of the trees and shrubs. 

Figure 7. Scenario 5 cul-de-sac with blocked trails. 

4. Scenario Background 

Fielded UGVs will operate in an almost infinite variety of terrain conditions. With the terrain 

available in Tactical Vehicle Maneuver Area-Bravo (TMVA-B) at FTIG, seven scenarios were 

devised on four separate areas of terrain. The objective in defining scenarios was to construct 

diverse situations that would defeat the AM capability (cause the XUV to back three times and 

call for operator assistance) but still allow the potential for the DR and the FCI planner to find, 

without operator intervention, an alternate route to the goal. 

Scenarios were constructed using the following approach. In each scenario, an SP and goal were 

selected such that given the a priori data, the global planner could generate a straightforward and 

easily achievable initial plan over the given terrain. Hundreds of XUV field runs previously 

conducted in this program had shown that unobstructed paths were normally completed with the 

AM planner. Then, to challenge all three mobility planners, each route was physically blocked 

to make XUV passage impossible along the intended route. The blockage was not available to 
10 

the global map and hence the initial plan, but rather had to be locally sensed by the XUV. The 

blocked passage was expected to force the planners to search for an alternate route to the goal. 

Each scenario and terrain area had unique challenges that are described in the following section. 

A limited use of a priori terrain information was provided to help shape each situation. In one 

scenario, false a priori data was introduced to suggest a likely alternate path that, if visited, 

would also be found impassible by the XUV. 

5. Experimental Approach 

5.1 Experimental Design 

An experimental design was constructed that could be executed both in simulations and in field 

runs at FTIG. This design included seven scenarios and three planners. A single replication of a 

full factorial design, crossing each scenario with each planner, would require 21 runs. The intent 

of the experimental design was to provide a span of cul-de-sac challenges for the planners that 

would fairly stress the new technology. Absolute measurement of performance in operational or 

field testing environments is elusive. Often, there is no single quantitative measure, such as time 

or distance, that adequately represents performance. Did the planner allow the XUV to 

overcome the specific blockage challenge and was navigation to the goal point achieved? These 

questions are answered with an affirmative or negative. 

To gain additional insight, expectations remained subjective on the performance of the XUV in 

the field. Subject matter experts (SMEs) evaluated the XUV behavior at various points along a 

course according to a relatively common expectation of performance. These expectations were 

forged in discussion prior to the experiment and helped determine the seven routes chosen. The 

expectations, prior to the field testing, were further solidified through a simulation study working 

toward a reference distribution of behavior for the XUV under the three planners and seven 

courses. Performance expectation is where the analysis starts for each scenario. 

5.2 Parametric Study in Simulation 

All seven scenarios were modeled in the RIVET environment, and a parametric study consisting 

of 63 runs was conducted in RIVET. Variables were scenario (seven levels) and planners (AM, 

DR, FCI) with three repetitions of each configuration. Tree/shrub density and distribution were 

not varied as additional conditions because of time constraints; although, it was conjectured that 

doing so may have introduced more realistic variation in the simulated outcomes. The RIVET 

runs were used to verify the performance of the planning algorithms as well as validate the 

experimenter’s expectations of the planners’ performance in each scenario. All 63 runs were 

executed in RIVET. The results of the RIVET runs are included in the following discussion.
11 

5.3 Field Experiment 

The field experiment was designed to replicate the RIVET parametric study but with additional 

replications because of expected variations in the XUV performance in the field environment. 

More replications would be required for trends in performance to become evident. The variables 

were scenario (seven levels), planner (three levels), and repetitions (five reps in the field). To 

save time and accomplish as many useful runs as possible, not all replications in each cell were 

executed. For the AM runs, when it became apparent that repeated runs using AM would 

produce identical failed results, fewer replications were done with that planner (see table 1). 

Table 1. The number of runs executed in RIVET and in the field. 

Scenario 

No. of Runs 

RIVET Field 

AM DR FCI AM DR FCI 

1 3 3 3 1 5 3 

2 3 3 3 4 4 4 

3 3 3 3 2 4 3 

4 3 3 3 — — — 

5 3 3 3 2 5 5 

6 3 3 3 1 5 5 

7 3 3 3 5 5 5 

Scenario 4, a variant of scenario 3 with additional a priori data, was not run in the field because it 

was believed that the slight difference in a priori data between scenarios 3 and 4 would not 

appreciably affect XUV performance for any planner. 

In scenarios 5 and 6, a successful solution of the cul-de-sac and subsequent travel to the goal 

resulted in a run of ~1800 m. To conserve experimental time, the runs were truncated after the 

XUV completely exited the cul-de-sac area in the “finger” and moved to the tank trail and a clear 

path to the goal. 

Several runs were rerun because of events unrelated to the performance of the planners. For 

example, a steering component was broken on one run, and in another run, unmapped water from 

a recent rain (not near the cul-de-sac) created a mobility barrier that caused an emergency stop 

(e-stop) for safety. 

5.4 Data Collected 

The data collected included configuration files, XUV text logs (csv and bin formats), observer 

comments, and bufMor (internal AM logs) files for each run. Vehicle logs from the XUV 

included time, easting, northing, status messages from XUV movement, and planned paths 

(easting and northing) from the global planner. The XUV logs are the basic data analyzed for 

this report.
12 

5.5 Data Reduction 

XUV logs were converted to Excel files, and duplicate plan entries were eliminated. For DR 

runs, the global planner generated and passed to the XUV more plans than the XUV could 

execute. Newly generated consecutive plans were compared, and if the new plan was within 5 s 

and 5 m in distance of the current plan, it was removed. This more accurately described the 

actual XUV performance. 

Following the completion of the experiment, notes from five observers were compared to 

establish which runs would be included in the analysis. 

5.6 Graphics for Each Run 

Graphics illustrating the XUV path and narratives describing each run are located in appendix A. 

Experimenters’ observations are located in appendix B. Figure 8 is an example of the run 

graphics. 

Figure 8. Sample graphic from run 76.
13 

6. Scenario 1 

6.1 Situation 

In scenario 1, the SP was on the trail next to the tree line on Dan Grove Highway (figure 9). The 

goal was ~300 m northeast of the start. A priori data included a trail from the SP to the goal, 

trees to the west of the trail, an a priori trail to the goal beyond the blockage, and a nuclear, 

biological, and chemical (NBC) area (XUV no go) along the main tank trail to the east. The 

NBC area was to prevent the XUV from taking the low-cost route along a main trail, 

encouraging it to consider another alternative route instead. The trail was blocked ~60 m from 

the SP in an area surrounded by trees and high brush. There was not room for the XUV to turn 

around at the point of blockage. Backing up was the only possible maneuver. 

Figure 9. Scenario 1 description. 

6.2 Expectations 

The expectations were that (1) AM would back up three times and need operator assistance, and 

(2) upon seeing the blockage, the DR and FCI would replan back up the trail and look for an 

opening to the east where an unmarked trail paralleled the original route and was open to the 

goal; potentially, on the DR runs, HMP would enable the XUV to turn around in this confined 

space. FCI would need to back up to the new planned path because of the lack of HMP 

interface.
14 

6.3 Results 

The following list describes the column headings in the results tables: 

• Scenario: 1–7. 

• Planner: AM, DR, FCI. 

• Source: RIVET or field. 

• Run: run number. 

• Plans: number of unique global plans found in the XUV log file. 

• Distance: distance XUV traveled in meters. 

• Total time: time from start to stop in minutes. 

• Time eng. off: time the engine was off (primarily from stalls). 

• Time HMP: time spent with HMP as the local planner. 

• Av spd kph: average speed entire run (dist/[total time – time eng. off]). 

• ET: elapse time (total time – time eng off). 

• Outcome: completed or DNF (did not finish). 

• Backups: number of times XUV backed up. 

Table 2. Scenario 1 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time 

HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

1 AM only Field 33 156.19 3.17 3.17 2.96 3 Stuck

1 AM only RIVET 238 151.54 2.40 0.00 2.40 3.79 3 Backups

1 AM only RIVET 239 152.71 2.59 0.00 2.59 3.53 3 Backups

1 AM only RIVET 240 155.13 2.39 0.00 2.39 3.90 3 Backups

1 DR Field 31.3 20 311.68 8.18 4.06 8.18 2.29 Complete

1 DR Field 35 15 369.98 17.56 5.53 17.56 1.26 4 Complete

1 DR Field 38 2 178.86 6.83 3.33 6.83 1.57 6 Stuck

1 DR Field 42 13 520.49 10.22 0.50 6.69 9.73 3.21 5 Complete

1 DR Field 43 5 134.06 5.63 1.91 5.63 1.43 1

p

HMP

1 DR RIVET 231 10 257.82 3.90 0.00 3.90 3.90 3.97 Complete

1 DR RIVET 232 13 261.07 3.19 0.00 3.16 3.19 4.91 1 Complete

1 DR RIVET 233 10 236.94 2.92 0.00 2.92 2.92 4.87 Complete

1 FCI Field 32 1 317.28 4.12 4.12 4.63 3 Complete

1 FCI Field 34 1 169.87 2.85 2.85 3.57 3 Backups

1 FCI Field 39 1 168.56 2.66 0.37 2.30 4.40 3 Backups

1 FCI RIVET 226 1 155.77 2.27 0.00 2.27 4.12 3 Backups

1 FCI RIVET 227 1 141.00 2.15 0.00 2.15 3.93 3 Backups

1 FCI RIVET 228 1 156.28 2.24 0.00 2.24 4.18 3 Backups
15 

6.4 Autonomous Mobility 

The three RIVET runs with AM planner resulted in identical behaviors. The XUV moved down 

the trail, saw the blockage, backed up three times, and the run was halted. 

One field run (run 33) with AM was conducted and similarly ended after max backups. AM was 

unable to find an alternate route. 

6.5 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors. The XUV moved down the 

trail and saw the blockage. HMP maneuvered the XUV back to the point where an opening 

existed to the southeast. The XUV then drove through the opening and discovered an unmarked 

trail to its left and parallel to the original plan. It followed that trail to the goal. In the RIVET 

runs, HMP was invoked 99.7% of the time on all three runs. All three RIVET runs achieved the 

goal. The orange triangles appearing in the figures of DR runs (figures A-3–A-8) represent the 

location of the XUV when a new global plan to the goal is accepted by the XUV. Hence, many 

new plans are observed in cul-de-sac situations, and fewer new plans are observed in less 

congested environments where the XUV is moving easily toward the goal. 

Three of the five field runs achieved the goal. Two of the three successful field runs, runs 31.3 

and 42, closely resembled the RIVET runs. For each, the XUV saw the blockage, returned to the 

opening on the southeast, discovered the unmarked trail to its left, and proceeded slowly down 

the unmarked trail under HMP control. The DR continuously generated plans that directed the 

XUV back to the original trail, beyond the blockage. However, the XUV was unable to find a 

clear path through the brush and trees, and eventually the DR gave a plan that followed the 

unmarked trail to the goal. On run 35, the XUV followed a similar path but this time found a 

clear path back to the original trail and completed the run on the original trail past the blockage. 

Runs 38 and 43 ended in e-stops when the HMP planner backed the XUV into an unknown area 

of dense brush. 

6.6 Field Cost Interface 

Under RIVET, all three FCI runs went as expected. None achieved the goal point. All FCI runs 

in RIVET saw the blockage, backed up three times, and the runs were halted. 

Field runs 34 and 39 were very similar to RIVET runs, stopping after three backups. On run 32, 

the XUV backed up three consecutive times without moving forward. (More typical of the 

backup behavior is for the XUV to move forward and probe the local terrain after changing the 

perspective via the backup.) This combined 60-m backup enabled it to discover the opening to 

the southeast and the unmarked trail paralleling the original path to the goal. The XUV 

proceeded down the unmarked trail to the goal. 
16 

7. Scenario 2 

7.1 Situation 

Scenario 2 is a derivation of scenario 1 with the same SP and goal (figure 10). The blockage was 

placed further north along the trail, after an opening to an alternate trail to the left that had a path 

to the goal. A priori data for scenario 2 is the same as scenario 1. There was not room for the 

XUV to turn around at the point of blockage. Backing up was the only possible maneuver. 

Figure 10. Scenario 2 description. 

7.2 Expectations 

The expectations were that (1) AM would back up three times and need operator assistance, 

ending the run for this experiment, and (2) upon seeing the blockage, the DR and FCI would 

replan back the trail and look for an opening to the west where a trail branched north from the 

original planned route and was open to the goal. Potentially, HMP would enable the XUV to 

turn around in this confined space, while FCI would need to back up to the new planned path 

because of the lack of HMP interface. 
17 

7.3 Results 

Table 3. Scenario 2 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

2 AM only Field 47 257.15 3.11 0.27 2.84 5.42 3 Complete

2 AM only Field 51 251.04 7.27 0.02 7.27 2.07 4 Complete

2 AM only Field 52 146.00 1.71 1.71 5.12 Complete

2 AM only Field 57 91.19 2.21 2.21 2.47 2 E-stop

2 AM only RIVET 241 189.70 2.80 0.00 2.80 4.07 3 Backups

2 AM only RIVET 242 182.28 2.54 0.00 2.54 4.31 3 Backups

2 AM only RIVET 243 185.03 2.55 0.00 2.55 4.36 3 Backups

2 DR Field 48 7 273.20 4.97 0.38 2.26 4.59 3.57 2 Complete

2 DR Field 49 7 368.24 8.47 5.80 8.47 2.61 2 Complete

2 DR Field 53 4 201.70 2.53 0.36 0.94 2.17 5.57 1 Complete

2 DR Field 56 5 91.32 3.01 0.20 0.91 2.81 1.95 E-Stop HMP

2 DR RIVET 206 5 326.57 4.15 0.00 3.16 4.15 4.73 Complete

2 DR RIVET 207 6 328.16 4.94 0.00 3.93 4.94 3.98 Complete

2 DR RIVET 208 6 345.55 5.90 0.00 5.05 5.90 3.51 Complete

2 FCI Field 46 1 189.29 5.52 5.52 2.06 4 Backups

2 FCI Field 50 1 394.78 8.37 8.37 2.83 6 Complete

2 FCI Field 54 1 132.78 1.61 1.61 4.95 Complete

2 FCI Field 55 1 174.75 4.69 4.69 2.24 4 E-stop

2 FCI RIVET 220 1 180.26 2.27 0.00 2.27 4.77 2 Backups

2 FCI RIVET 221 1 181.82 2.46 0.00 2.46 4.44 3 Backups

2 FCI RIVET 222 1 243.02 3.43 0.00 3.43 4.25 5 Backups

7.4 Autonomous Mobility 

The three RIVET runs with AM planner resulted in identical behaviors. The XUV moved down 

the trail, saw the blockage, and backed up three times. The runs were ended after three 

consecutive backups. AM was unable to find an alternate route. 

Three of the four AM field runs (47, 51, and 52) were able to successfully, but unexpectedly, 

find an alternate route to the east of the blockage. This was probably the result of sparse enough 

brush in the area for AM to find a local path and move the XUV through to the goal. With each 

successful run through the brush, the next run became easier for the planner to find a path 

through the brush. Run 57 ended in an e-stop when the XUV backed too close to a HMMWV 

used for blockage. 

None of the field runs for AM were similar to the RIVET runs. 

7.5 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors. The XUV moved down the 

trail and saw the blockage. HMP maneuvered the XUV back to the point where an opening 

existed to a trail to the east. The XUV then followed the trail to the goal. In the RIVET runs, 

HMP was invoked 76%–86% of the time.
18 

Three of the four field runs achieved the goal. On runs 48, 49, and 53, the XUV saw the 

blockage, generated new plans to the east of the blockage, and found its way to the goal. No 

plans were generated on these runs that would have utilized the trail to the west, leading to 

speculation that the alternate trail was not in the a priori data as it was in RIVET. As with AM, 

the more trips through the shrubs, the wider and easier the path became. It is unclear whether the 

NBC area intended as part of the a priori data was set for all runs. These three successful runs, 

upon taking the alternative path to the east, continued east to a main tank trail that should have 

been avoided due to an NBC presence. In run 56, the XUV was e-stopped after two backups 

because it backed into dense shrubs. 

None of the field runs for DR resembled the RIVET runs. 

7.6 Field Cost Interface 

None of the three RIVET runs achieved the goal point; all RIVET runs ended after three backups 

shortly after encountering the blockage. Again, this was unexpected as there should have been a 

path to the west in the a priori data. 

Runs 50 and 54 achieved the goal. These runs were similar to the successful DR and AM runs 

because the XUV saw the blockage, moved off the trail to the east of the blockage through high 

shrubs, and found its way to the goal. As with previous runs, the more trips through the shrubs, 

the wider and easier the path became. Run 46.2 was e-stopped because of max backups. Run 

55.1 was e-stopped because it backed too close to the HMMWV used in the blockage. 

One field run for FCI was similar to the three unsuccessful RIVET runs. 

8. Scenario 3 

8.1 Situation 

In scenario 3, the SP was on the trail at the base of a hill to the north of the helicopter pad (figure 

11). The goal was on the helicopter pad, and there was an open trail from the SP up the hill 

directly to the goal. The trail was deeply rutted, steep, and surrounded by high shrubs on each 

side, and was included in the a priori data. An alternate, unmarked trail existed to the west of the 

original path and a “false” trail to the east of the original plan was included in the a priori data. 

This false a priori trail was not passable by the XUV. The planned path was blocked ~80 m from 

the SP on the steepest part of the deeply rutted trail with high shrubs on both sides. There was 

not room for the XUV to turn around at the point of blockage. Backing up was the only possible 

maneuver. The only possible path to the goal was an unmarked trail to the west of the original 

plan.
19 

Figure 11. Scenario 3 description. 

8.2 Expectations 

The expectations were that (1) AM would back up three times and need operator assistance, and 

(2) upon seeing the blockage, the DR and FCI would replan back the trail and look for an 

opening to the east where an a priori trail gave a path to the goal, discover that this way was 

impassible, and then search for a path to the west of the original plan, hopefully finding the 

unmarked trail to the goal. 

8.3 Results 

Table 4. Scenario 3 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time 

HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

3 AM only Field 63 210.30 2.89 0.25 2.65 4.77 3 Backups

3 AM only Field 64 293.85 3.67 0.23 3.44 5.13 3 Backups

3 AM only RIVET 293 1 142.57 1.87 0.00 1.87 4.57 3 Backups

3 AM only RIVET 294 1 144.37 1.97 0.00 1.97 4.39 3 Backups

3 AM only RIVET 295 1 142.29 2.01 0.00 2.01 4.25 3 Backups

3 DR Field 62 8 139.31 18.35 10.52 18.35 0.46 1

p

HMP

3 DR Field 65 19 446.51 12.97 9.51 12.97 2.07 3 E-stop

3 DR Field 68 21 187.07 6.54 0.26 5.57 6.28 1.79

p

HMP

3 DR Field 72 3 95.02 0.67 0.02 0.17 0.65 8.73 E-stop

3 DR RIVET 290 6 253.34 2.60 0.00 1.95 2.60 5.85 Complete

3 DR RIVET 291 8 241.73 2.65 0.00 2.05 2.65 5.48 Complete

3 DR RIVET 292 8 282.77 3.64 0.00 2.06 3.64 4.67 2 Complete

3 FCI Field 61 1 310.97 4.65 4.65 4.02 3 Complete

3 FCI Field 66 1 268.25 4.30 0.47 3.83 4.21 6 Backups

3 FCI Field 69 1 339.58 4.94 0.25 4.69 4.35 4 Complete

3 FCI RIVET 286 1 959.97 15.01 0.00 15.01 3.84 27 Backups

3 FCI RIVET 287 1 201.96 2.35 0.00 2.35 5.15 3 Backups

3 FCI RIVET 288 1 519.61 8.07 0.00 8.07 3.86 13 Backups
20 

8.4 Autonomous Mobility 

The three RIVET runs with AM planner resulted in identical behaviors. The XUV moved up the 

trail, saw the blockage, and backed up three times. The runs were ended after three consecutive 

backups. AM was unable to find an alternate route. 

The two field runs with AM planner (63 and 64) resulted in behavior identical to the RIVET 

runs. The XUV moved up the trail, saw the blockage, and backed up three times. The trail at the 

point of the blockage was steep and deeply rutted, making any maneuver difficult. The runs 

were ended after three consecutive backups. AM was unable to find an alternate route. 

8.5 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors (figure 12). The XUV moved 

up the trail and saw the blockage. HMP maneuvered the XUV back to the point where an 

opening existed to the west. DR then generated successive plans to move the XUV to the goal 

through an unmarked trail. The a priori road to the east was not explored. In the RIVET runs, 

HMP was invoked 57% to 77% of the time. 

Figure 12. RIVET run 290. 

None of the four field runs achieved the goal. On runs 62 and 68, the XUV saw the blockage and 

generated new plans back to the point where an opening existed to the west. However, HMP was 

unable to maneuver the XUV back down the steep trail, and both runs were e-stopped when HMP 

backed into high brush. During run 72, the XUV went through the barrels at the blockage and was 

e-stopped. On run 65, the XUV maneuvered back to the base of the hill and explored the false 
21 

a priori trail to the east, finding it impassable. It then generated plans to the west of the original 

plan toward the unmarked trail to the goal. However, the XUV failed to find the small opening 

in the brush for the unmarked trail and was e-stopped short of the goal. In the field runs, HMP 

was invoked 25% to 85% of the time. 

None of the field runs resembled the RIVET runs. 

8.6 Field Cost Interface 

The three RIVET runs with the FCI planner resulted in identical behaviors. The XUV moved up 

the trail, saw the blockage, and backed up three times until the run was halted for max backups. 

Two of the three FCI field runs achieved the goal. On runs 61 and 69, the XUV saw the 

blockage, backed to the base of the hill, and immediately explored the area to the west of the 

original plan, discovering the unmarked trail to the goal. In run 66, the XUV moved up the trail, 

saw the blockage, and backed up six times until the run was halted for max backups. 

9. Scenario 4 

9.1 Situation 

Scenario 4 was exactly like scenario 3, with the exception of additional a priori data on trees and 

shrubs near the alternate unmarked trail that existed to the west of the original path (figure 13). 

It was thought that additional a priori data may increase the probability of finding the unmarked 

trail to the goal. 

Figure 13. Scenario 4 description.
22 

9.2 Expectations 

The expectations were that (1) AM would back up three times and need operator assistance, and 

(2) upon seeing the blockage, the DR and FCI would replan back the trail and look for an 

opening to the east where an a priori trail gave a path to the goal, discover that this way was 

impassible, and then search for a path to the west of the original plan, hopefully finding the 

unmarked trail to the goal with the assistance of additional a priori tree data. 

9.3 Results 

Table 5. Scenario 4 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

4 AM only RIVET 283 1 143.09 1.94 0.00 1.94 4.43 3 Backups

4 AM only RIVET 284 1 145.30 1.92 0.00 1.92 4.54 3 Backups

4 AM only RIVET 285 1 136.00 2.00 0.00 2.00 4.08 3 Backups

4 DR RIVET 280 9 241.34 2.39 0.00 1.69 2.39 6.05 Complete

4 DR RIVET 281 6 247.78 2.41 0.00 1.88 2.41 6.16 Complete

4 DR RIVET 282 9 238.60 2.28 0.00 1.50 2.28 6.29 Complete

4 FCI RIVET 275 1 393.16 5.52 0.00 5.52 4.27 9 Backups

4 FCI RIVET 276 1 201.17 2.41 0.00 2.41 5.01 3 Backups

4 FCI RIVET 277 1 202.55 2.34 0.00 2.34 5.20 3 Backups

9.4 Autonomous Mobility 

Scenario 4 was run only in RIVET. The three RIVET runs with AM planner resulted in identical 

behaviors. The XUV moved up the trail, saw the blockage, and backed up three times. The runs 

were ended after three consecutive backups. AM was unable to find an alternate route 

9.5 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors. The XUV moved up the trail 

and saw the blockage. HMP maneuvered the XUV back to the point where an opening existed to 

the west. DR then generated successive plans to move the XUV to the goal through an 

unmarked trail. 

9.6 Field Cost Interface 

The three RIVET runs with the FCI planner resulted in identical behaviors. The XUV moved up 

the trail, saw the blockage, and backed up three times until the run was halted for max backups.
23 

10. Scenario 5 

10.1 Situation 

In scenario 5, the SP was at the opening of the terrain area known as the “finger” in the “football 

field” (figure 14). The goal was on the main tank trail to the west of the cul-de-sac, ~400 m west 

of the SP. The initial plan was along the a priori trail toward the goal, which narrowed into a 

one-lane trail for ~200 m, then ended on the main tank trail where the goal was about 50 m 

north. A priori data included trails from the SP to the goal, trees to the north and south of the 

trail, an alternate a priori trail at the base of the “finger” north of the original path, and trees 

between the two a priori trails. The planned trail was blocked where the trail narrowed, and the 

alternate a priori trail was also blocked where it entered the trees. This situation constituted a 

very large, open cul-de-sac with plenty of maneuver room for the XUV. 

Figure 14. Scenario 5 description. 

10.2 Expectations 

One expectation was that AM would see the blockage and repeatedly circle, trying to find a path 

through the original plan. For this reason, a “three circle” rule was instituted to end a run if the 

XUV did not appear to be finding a solution after circling three times and/or backing up three 

times and calling for operator assistance. Another expectation was that upon seeing the initial 

blockage, the DR and FCI would replan to the nearby alternate trail. Upon finding it blocked, 

DR and FCI were expected to plan for the XUV to return the way it came and discover an 

alternative way on the trail around the trees and shrubs to the east.
24 

10.3 Results 

Table 6. Scenario 5 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

5 AM only Field 16 265.76 9.53 0.32 9.21 1.73 1 circles

5 AM only Field 20 451.59 4.93 4.93 5.50 2 circles

5 AM only RIVET 234 1 248.93 2.53 0.00 2.53 5.91 2 Backups

5 AM only RIVET 235 248.49 2.47 0.00 2.47 6.03 3 Backups

5 AM only RIVET 236 293.74 3.31 0.00 3.31 5.33 3 Backups

5 DR Field 18 14 653.39 4.63 2.15 4.63 8.47 1 Complete

5 DR Field 19 17 824.49 11.94 4.92 11.94 4.14 Complete

5 DR Field 22 8 682.36 4.92 2.14 4.92 8.32 Complete

5 DR Field 27 20 727.25 6.94 0.32 3.70 6.62 6.59 Complete

5 DR Field 30 13 757.55 7.16 2.02 7.16 6.35 5 Complete

5 DR RIVET 23 16 635.43 5.78 0.00 3.10 5.78 6.59 Complete

5 DR RIVET 24 10 689.19 5.19 0.00 2.54 5.19 7.97 Complete

5 DR RIVET 201 10 627.00 5.01 0.00 2.69 5.01 7.51 1 Complete

5 FCI Field 17 1 336.93 3.51 0.07 3.44 5.87 E-stop

5 FCI Field 21 1 494.46 9.79 9.79 3.03 E-stop

5 FCI Field 23 1 923.50 12.36 12.36 4.48 1 Complete

5 FCI Field 25 2 1248.00 15.56 15.56 4.81 10 circles

5 FCI Field 28 1 801.98 5.53 5.53 8.71 1 Complete

5 FCI RIVET 12 1 735.67 5.73 0.00 5.73 7.70 2 Complete

5 FCI RIVET 13 1 725.60 5.19 0.00 5.19 8.38 1 Complete

5 FCI RIVET 215 1 664.26 4.87 0.00 4.87 8.18 1 Complete

10.4 Autonomous Mobility 

All three RIVET runs were ended on max backups immediately upon encountering the blockage 

on the initial planned path. AM would see the blockage of the original path, back up, and be 

unable to find a plan through the blockage. 

In the two field runs (16.1 and 20) with the AM planner, the XUV repeatedly circled in the 

cul-de-sac, trying to find an acceptable way along the planned path. After circling three or more 

times, the runs were e-stopped. 

10.5 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route, saw the path blocked, and 

attempted to go through the alternate route nearby to the north but saw that it also was blocked. 

The XUV then replanned back east past the starting point, around the a priori trees, and south 

toward the tank trail and a clear path to the goal. These runs spent 49%–54% of the time in 

HMP at the cul-de-sac while generating and trying alternate plans. 

The five field runs resembled very closely the RIVET runs. The field runs tended to spend 

additional time and distance exploring alternate routes in the cul-de-sac, probably because of the 

highly varied vegetation on/around the alternate paths out of the cul-de-sac. These runs spent 

28%–53% of the time in HMP at the cul-de-sac while generating and trying alternate plans.
25 

10.6 Field Cost Interface 

In the three RIVET runs, the XUV preceded along the planned route, saw the path blocked, and 

circled several times while attempting to find a way through the original and alternate trails. The 

XUV eventually replanned back past the starting point, around the a priori trees, and south 

toward the tank trail and a clear path to the goal. 

Two of the five field runs were successful in finding an alternate route. Runs 23 and 28 were 

successful in achieving the goal but were 100–200 m longer than the corresponding RIVET runs 

because these runs spent additional time and distance circling in the cul-de-sac and building up 

local terrain data before deciding to replan back past the starting point, around the a priori trees 

and south toward the tank trail and a clear path to the goal. Three of the runs (21.1, 25.2, and 17) 

saw the blockage and spent additional time and distance circling in the cul-de-sac, but FCI never 

generated a plan back toward the SP. The runs were ended after three or more circles. 

Two of the five field runs resembled very closely the RIVET runs. 

11. Scenario 6 

11.1 Situation 

Scenario 6 was exactly the same as scenario 5 with one exception. The a priori tree data was 

shortened by ~100 m in the southerly direction (figure 15). This was done to allow the XUV to 

explore for alternate paths through this wooded area, which had several unmarked trails through 

to the tank trail on the southern side. Plans and blockages remained, as in scenario 5. 

Figure 15. Scenario 6 description.
26 

11.2 Expectations 

The expectations were that (1) AM would see the blockage and repeatedly circle, trying to find a 

path through the original plan, and (2) upon seeing the initial blockage, the DR and FCI would 

replan to the nearby alternate trail. Upon finding it blocked, DR and FCI were expected to plan 

for the XUV to return the way it came and discover an alternative way through the trees and 

shrubs to the south. 

11.3 Results 

Table 7. Scenario 6 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time 

HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

6 AM only Field 78 480.19 3.70 3.70 7.78 1 circles

6 AM only RIVET 261 410.04 4.92 0.00 4.92 5.00 5 Backups

6 AM only RIVET 262 452.09 5.07 0.00 5.07 5.35 6 Backups

6 AM only RIVET 263 679.10 9.40 0.00 9.40 4.34 12 Backups

6 DR Field 77.1 20 722.00 19.69 7.98 19.69 2.20 4 Complete

6 DR Field 80 18 605.20 7.15 5.51 7.15 5.08 Complete

6 DR Field 84 25 719.32 18.73 5.79 18.73 2.30 4 Complete

6 DR Field 86 19 557.95 6.01 3.07 6.01 5.57 1 Complete

6 DR Field 89 17 560.73 5.23 3.10 5.23 6.43 Complete

6 DR RIVET 258 15 409.07 3.20 0.00 1.36 3.20 7.67 Complete

6 DR RIVET 259 14 422.67 3.10 0.00 1.25 3.10 8.18 Complete

6 DR RIVET 260 15 448.44 3.82 0.00 1.52 3.82 7.05 Complete

6 FCI Field 76 1 896.50 7.89 7.89 6.82 Complete

6 FCI Field 79.1 1 674.38 5.27 5.27 7.68 1 Complete

6 FCI Field 82 1 684.42 4.66 4.66 8.81 Complete

6 FCI Field 85 1 1103.38 9.69 9.69 6.83 1 Complete

6 FCI Field 88 1 592.16 3.90 3.90 9.11 Complete

6 FCI RIVET 251 1 511.35 3.95 0.00 3.95 7.76 Complete

6 FCI RIVET 254 1 589.00 4.84 0.00 4.84 7.30 3 Complete

6 FCI RIVET 255 1 526.46 3.87 0.00 3.87 8.16 1 Complete

11.4 Autonomous Mobility 

All three RIVET runs ended on max backups after circling and repeatedly trying to go through 

the blocked area. 

In the one field run with AM, the XUV repeatedly circled in the cul-de-sac, trying to find an 

acceptable way along the initial planned path. After circling three or more times, the run was 

stopped. 

11.5 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route, saw the path blocked, and 

attempted to go through the alternate route nearby to the north but saw that it also was blocked. 

The XUV then replanned back past the starting point and discovered a path through the trees 

south toward the tank trail and a clear path to the goal. These runs spent 40%–42% of the time 

in HMP at the cul-de-sac while generating and trying alternate plans.
27 

The five field runs resembled very closely the RIVET runs. The field runs tended to spend 

additional time and distance exploring alternate routes in the cul-de-sac, probably because of the 

highly varied vegetation on/around the alternate paths out of the cul-de-sac. These runs spent 

31%–77% of the time in HMP at the cul-de-sac while generating and trying alternate plans 

11.6 Field Cost Interface 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, and circled several times while attempting to find a way through the original and 

alternate trails. The XUV eventually replanned back past the starting point and discovered a 

route through the trees south toward the tank trail and a clear path to the goal. 

All five field runs were successful in finding an alternate route similar to the runs in RIVET. 

These runs spent additional time and distance circling in the cul-de-sac, building up local terrain 

data before replanning back past the starting point and discovering a path through the trees south 

toward the tank trail and a clear path to the goal. 

All five of the field runs resembled very closely the RIVET runs. 

12. Scenario 7 

12.1 Situation 

Scenario 7 was the simplest scenario (figure 16). The SP and goal were along a tree line in the 

football field. The trail to the goal went between a solid tree line to the north and a small set of 

trees and shrubs to the south. Both sets of trees were in the a priori data. The path between the 

trees was blocked ~50 m into the tree line. 

Figure 16. Scenario 7 description.
28 

12.2 Expectations 

The expectations were that (1) AM would see the blockage and repeatedly back up because of 

the heavy foliage on both sides of the trail (it was expected that max backups would end these 

runs) and (2) upon seeing the initial blockage, the DR and FCI would replan back to the split in 

the terrain and find the alternate route to the goal. 

12.3 Results 

Table 8. Scenario 7 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

7 AM only Field 2 253.83 3.34 0.30 3.05 5.00 2 E-stop

7 AM only Field 4 1 341.02 5.28 5.28 3.87 3 Complete

7 AM only Field 8 206.87 0.99 0.99 12.48 Complete

7 AM only Field 10 175.14 2.71 2.71 3.88 3 E-stop

7 AM only Field 13 1 211.05 2.91 0.46 2.44 5.19 3 Max Backups

7 AM only RIVET 248 495.66 3.45 0.00 3.45 8.61 Complete

7 AM only RIVET 249 530.77 3.79 0.00 3.79 8.40 Complete

7 AM only RIVET 250 921.04 8.01 0.00 8.01 6.90 2 Complete

7 DR Field 1 11 432.62 7.37 4.42 7.37 3.52 5 Complete

7 DR Field 6 8 331.99 6.11 4.18 6.11 3.26 Complete

7 DR Field 7 16 368.12 12.73 5.73 12.73 1.73 1 E-stop

7 DR Field 11 13 260.12 19.40 16.52 19.40 0.80 3 E-Stop HMP

7 DR Field 15 19 365.36 9.62 4.65 9.62 2.28 6 Max Backups

7 DR RIVET 212 11 282.32 3.07 0.00 2.21 3.07 5.53 Complete

7 DR RIVET 213 10 296.26 2.46 0.00 1.50 2.46 7.24 Complete

7 DR RIVET 214 14 281.37 3.34 0.00 2.68 3.34 5.06 Complete

7 FCI Field 3 1 212.12 2.85 0.24 2.62 4.86 3 Max Backups

7 FCI Field 5 1 193.94 2.52 2.52 4.62 3 Max Backups

7 FCI Field 9 1 193.41 3.38 0.24 3.14 3.69 3 Max Backups

7 FCI Field 12 2 186.52 2.74 0.24 2.51 4.47 3 Max Backups

7 FCI Field 14 1 213.63 3.16 3.16 4.05 3 Max Backups

7 FCI RIVET 17 1 266.89 1.49 0.00 1.49 10.74 Complete

7 FCI RIVET 224 1 286.07 2.00 0.00 2.00 8.57 Complete

7 FCI RIVET 225 1 291.51 2.06 0.00 2.06 8.49 Complete

12.4 Autonomous Mobility 

All three RIVET runs were successfully completed as the XUV sensed the blockage and turned 

around 180° to return to the place in the trail where an alternative trail existed to the goal. The 

RIVET-modeled foliage around the blockage allowed this maneuvering. This was not expected. 

The field runs varied considerably. Run 8 completely ignored the original path and skirted to the 

south of the cul-de-sac directly to the goal. Run 4 found the cul-de-sac but backed up three times 

and was then positioned in a location where it was able to find an open path along the trail to the 

west. From that point, it went directly to the goal. Run 2 was e-stopped at the blockage site, run 

10 was e-stopped when the XUV backed into a rock, and run 13 was ended for max backups. 

Although two field runs achieved the goal, none did so in the manner of RIVET, where the XUV 

was able to turn in the simulated environment.
29 

12.5 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, and planned a way out. To achieve the plan, HMP directed a 180° turn, and the XUV 

followed a new plan back the trail and around the southern side of the trees to the goal. 

In the field runs, after the blockage was encountered, HMP backed the XUV the entire distance 

out of the cul-de-sac on four of the five runs. Two of those runs ended in completions. All of 

the remaining three runs were able to escape the cul-de-sac but were unable to complete the run. 

A suspected navigation problem ended run 7 after HMP executed a k-turn and retreated from the 

blockage, and an HMP problem ended run 11. Run 15 ended for max backups. 

12.6 Field Cost Interface 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, circled for a 180° turn out of the cul-de-sac, and followed a new plan back the trail and 

around the southern side of the trees to the goal. As with the AM RIVET runs, the modeled 

terrain allowed room for the turn. 

In the five field runs, the XUV encountered the blockage and backed up three times but not far 

enough to enable the XUV to see the unobstructed path to the goal below the tree line. All five 

runs ended with max backups. 

13. Discussion and Analysis 

13.1 Analytical Approach 

Analysis of UGV planner performance is extremely challenging for several reasons, such as the 

nature of a field test, limited control over a key explanatory variable, the lack of a definitive 

performance measure, and the overall complexity of the system. Field experimentation always 

introduces greater variability due to noise variables not present in the sterile surroundings in 

most development testing. This problem is magnified when testing UGV planners because 

variables one might think of as noise (e.g., nuances in the foliage, ground undulations) can 

influence perception/planning in ways unanticipated by experimenters, sending the UGV along a 

route not previously considered. 

Beyond local nuances in the terrain, there is the challenge of finding the exact set of macro 

terrain conditions necessary to build a meaningful scenario. Even at FTIG, a relatively large and 

rich area for terrain variation, finding conditions that will stress planner reasoning (different for 

each planner) so that successes are earned and failures can be learned from is difficult. When 

such a scenario is seen in the existing terrain, it usually requires dressing up in some manner with 

other obstacles or a priori data to force the planner to solve an intended problem rather than work 
30 

around the new challenge by employing an already proven planning capability. Previous to this 

study, the validity of the planned scenario to provide the intended challenge was only in the eyes 

of an SME. 

A definitive performance measure remains elusive. The goal should be to assess the decision 

making of the planner as it progresses toward the goal. However, evaluation at markers over the 

length of a course according to some scoring rubric is infeasible because, due to unanticipated 

branching by the UGV, there is no way for the evaluation plan to consider all paths, unless in a 

post hoc manner. The UGV might avoid a number of those markers. Time and distance 

measures would be of little value. The objective measure remaining is only whether or not the 

UGV overcame the intended challenge and reached the goal. A subjective measure might be 

possible to score the degree to which the UGV behavior on a scenario was consistent with SME 

expectation, but that was not attempted here. 

Finally, the complexity of the technology heightens the significance of the three reasons just 

discussed. To begin, there are a number of settings related to perception and planning that can 

be tuned to a specific environment (e.g., the previously mentioned CellScale in FCI). Even in 

AM, numerous local planning costs considered make finding suitable scenarios difficult. When 

costs are expanded to the global decision space by FCI and DR, the SME must rely on a notional 

understanding of the planners’ intent. 

With that in mind, the focus of the analysis was on the expected behavior of the UGV in wellconsidered scenarios and whether or not the planner overcame the principal challenge and 

reached the goal. A collective SME notional understanding of expected behavior influenced the 

scenario choices and the planned dressing up of those routes in the global map or in the field. 

RIVET was used to reinforce and augment the notional understanding and was valuable in 

confirming the scenarios, but fell short of providing the definitive reference distribution for 

behavior. While actual performance is measured and compared to RIVET results, analysis 

looked beyond RIVET for explanations of observed UGV behavior when appropriate. 

13.2 Expectations in RIVET 

An initial question to consider is, Did the planners perform in RIVET as expected in each 

scenario? All three RIVET runs in each configuration gave identical results, so RIVET provided 

no variation in performance within a set of experimental conditions. Table 9 shows the 

percentage of runs in RIVET that matched the experimenter’s expectations, which were that AM 

would fail to reach the goal and that DR and FCI runs would solve the cul-de-sac and reach the 

goal. In 15 of 18 experimental conditions (scenario 4 is not considered), the planners performed 

as expected, except for FCI in scenarios 1–3. In each exception, the trail where the blockage was 

placed was very constrained, leaving the XUV with only the choice of backing up when the 

block was encountered. The scenarios were constructed so that backing up three times would not 
31 

Table 9. RIVET runs achieving goal point. 

Planner Result 1 2 3 5 6 7

AM RIVET 0/1 (0%) 0/1 (0%) 0/1 (0%) 0/1 (0%) 0/1 (0%) 0/1 (0%)

DR RIVET 3/3 (100%) 3/3 (100%) 3/3 (100%) 3/3 (100%) 3/3 (100%) 3/3 (100%)

FCI RIVET 0/3 (0%) 0/3 (0%) 0/3 (0%) 3/3 (100%) 3/3 (100%) 3/3 (100%)

Scenario

Note: Red indicates 0%–24% completed. 

ordinarily enable the XUV to observe an alternative route; hence, each FCI run in these scenarios 

ended in a stop for max backups. The lack of an HMP interface to FCI probably contributed to 

this failure to maneuver to a location where an alternate route could be observed. These results, 

obtained before the field portion of the experiment, confirmed the validity of the scenarios to the 

extent possible in simulation. 

13.3 Expectations in the Field 

A second question to consider is, Did the planners perform in the field runs as expected in each 

scenario? There was considerable variation in the routes taken and the outcomes of the field 

runs. 

13.3.1 Reaching the Goal Point 

Table 10 shows the outcomes of the runs in the field. “Complete” means that the XUV reached 

the intended goal after overcoming the obstacles placed in the original planned path. Color 

codes are used to illustrate the percentage of runs achieving the goal point. 

Table 10. Field runs reaching the goal point. 

Planner Result 1 2 3 5 6 7 Subtotal

Complete Complete Complete Complete Complete Complete 5/15

Field 0/1 (0%) 3/4 (75%) 0/2 (0%) 0/2 (0%) 0/1 (0%) 2/5 (40%) (33%)

Total

Complete Complete Complete Complete Complete Complete 18/28 35/68

Field 3/5 (60%) 3/4 (75%) 0/4 (0%) 5/5 (100%) 5/5 (100%) 2/5 (40%) (64%) (51%)

Complete Complete Complete Complete Complete Complete 12/25

Field 1/3 (33%) 2/4 (50%) 2/3 (67%) 2/5 (40%) 5/5 (100%) 0/5 (0%) (48%)

AM

DR

FCI

Scenario

Note: Green indicates 75%–100% match, yellow is 25%–74% match, and red is 0%–24% completed.
32 

AM runs went about as expected. Four of the scenario cul-de-sacs stumped the XUV totally. 

Scenario 2 was solved by AM because the trail prior to the block was not so dense with brush 

that AM could not find an alternate route around the block through the brush and trees. DR and 

FCI also benefitted from this location; each planner, when successful, chose the identical bypass 

of the cul-de-sac. DR reached the goal point on 64% of its runs but completely failed scenario 3, 

where it was expected to retreat from a cul-del-sac on a narrow trail on a steep hill. Two runs 

were e-stopped when HMP backed the XUV into trees, and the third run was e-stopped when the 

XUV went through the barrels blocking the trail. FCI was less effective (48%) than DR and 

completely failed in scenario 7, where it could never see a clear path after three backups. 

Six runs did not achieve the goal point but did overcome the blockage and solve the cul-de-sac 

problem. After getting past the blockage, runs 2, 7, 11, 38, 46.2, and 65 experienced other 

mobility problems that caused an e-stop before reaching the goal. Figure 17 illustrates this point 

with run 7 of scenario 7. In this run, the DR solved the cul-de-sac (upper right) and moved 

toward the goal point, generating several new plans along the way until it lost the NAV solution 

and the run was e-stopped. At the point of the e-stop, the XUV had a viable plan to the goal. 

Figure 17. Run 7 where XUV solves cul-de-sac but does not achieve the goal point. 
33 

Table 11 shows the percentage of runs that solved the cul-de-sac regardless of whether or not the 

XUV reached the goal point. 

Table 11. Field runs solving the cul-de-sac. 

Planner 1 2 3 5 6 7 Subtotal

Result Solved Solved Solved Solved Solved Solved 6/15

Field 0/1 (0%) 3/4 (75%) 0/2 (0%) 0/2 (0%) 0/1 (0%) 3/5 (60%) (40%)

Total

Result Solved Solved Solved Solved Solved Solved 22/28 41/68

Field 4/5 (80%) 3/4 (75%) 1/4 (25%) 5/5 (100%) 5/5 (100%) 4/5 (80%) (79%) (60%)

Result Solved Solved Solved Solved Solved Solved 13/25

Field 1/3 (33%) 3/4 (75%) 2/3 (67%) 2/5 (40%) 5/5 (100%) 0/5 (0%) (52%)

AM

DR

FCI

Scenario

Note: Green indicates 75%–100% match, yellow is 25%–74% match, and red is 0%–24% completed. 

Results suggest DR and FCI were very effective at solving the cul-de-sacs, DR at 79% and FCI 

at 52%. The percentage of completed runs and the percentage of runs that “solved” the 

cul-de-sac are shown in figure 18. 

Figure 18. Comparison of completed runs vs. solved runs. 

Completed Run and Solved cul-de-sacs

33%

64%

48%

14%

100%

43%

40%

79%

52%

14%

100%

43%

0%

20%

40%

60%

80%

100%

AM DR FCI AM DR FCI

Field Field Field RIVET RIVET RIVET

Planner

Percent

Completed Runs

Solved Runs
34 

13.3.2 AM Observations 

Despite attempts to design them otherwise, scenarios 2 and 7 turned out to be solvable by AM. 

AM runs were successful in scenario 2 because the shrubs and trees along the trail near the 

blockage were not sufficiently dense to keep the XUV from finding an opening after it backed 

away from the block. In scenario 7, AM never entered the cul-de-sac on one run and was able to 

back completely out of the cul-de-sac on a second run. Except for these instances, the AM runs 

could not solve the scenarios. 

13.3.3 DR Observations 

With the exception of scenario 3, DR runs achieved the goal on 18/24 (75%) of the runs and 

solved 21/24 (88%) of the cul-de-sacs. This is very good performance given the difficulty of the 

scenarios. The steep terrain and rutted trail on scenario 3 prohibited the HMP from maneuvering 

the XUV well at the point of the blockage. On the one run when the XUV was able to maneuver 

and generate new plans, it failed to find the small opening leading to an unmarked trail up the 

hill to the goal. In scenario 7, the failed run had navigation and HMP issues discussed in 

appendix A. It could be argued that success in scenario 2 might have been, as with AM, more a 

local planning achievement, but as new global routes pulled it in that direction DR is credited. 

13.3.4 FCI Observations 

FCI runs were successful about half the time (48%). Scenario 7 was singularly difficult for FCI. 

In all five scenario 7 runs, the XUV backed up three times but was not able to observe an 

alternative route; hence, each of the FCI runs in this scenario ended in a stop for max backups. 

The lack of an HMP interface probably contributed to this failure to maneuver to a location 

where an alternate route could be observed. The same factors applied to failed runs in 

scenarios 1–3. The same caveat for successful scenario 2 performance for DR could be made for 

FCI, but FCI is credited for the three successes. 

13.4 Scenario Difficulty 

Each scenario had its own particular level of difficulty attributable to the size of the cul-de-sac, 

the maneuver room available, the density of the trees and shrubs, etc. When data was used from 

only the DR and FCI runs, and based on the percentage of runs completed, scenarios 3 and 7 

were the most difficult for the planners to solve (see figure 19). The blockage in scenario 3 was 

on a narrow trail on a steep hill—a very difficult mobility problem for the XUV. Scenario 7 

difficulty was caused by the depth of the cul-de-sac and the lack of maneuver room near the 

blockage. The XUV was often not able to turn around or back up far enough to exit the cul-desac. 
35 

Figure 19. Scenario difficulty (percent goal achieved DR and FCI only). 

14. Excursion Scenario 1.1 

14.1 Situation 

Scenario 1.1 was the same as scenario 1 except that the NBC area was removed in order to see if 

the DR would plan to use the low-cost main tank trail to the east as an alternate route (figure 20). 

Added as an excursion during the field experiment, scenario 1.1 was not run in RIVET. 

Figure 20. Scenario 1.1 description.

scenario difficulty (DR and FCI only)

50.0%

62.5%

28.6%

70.0%

100.0%

20.0%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

123567

Scenario #

% runs achieved goal
36 

14.2 Dynamic Replanner 

On neither run did the DR plan a path that utilized the main tank trail to the east. Run 116 

(figure 21) was typical of the DR runs 31.3 and 42, where the XUV saw the blockage, returned 

to the opening on the right, discovered the unmarked trail to the left, and proceeded slowly down 

the unmarked trail under HMP control. The DR generated plans that directed the XUV back to 

the original trail, beyond the blockage. However, the XUV was unable to find a clear path 

through the brush and trees, and eventually the DR gave a plan that followed the unmarked trail 

to the goal. On run 114 (figure 21), the XUV was e-stopped when the HMP backed it into an 

unknown area of high brush near the blockage. 

Figure 21. Scenario 1.1 runs with no NBC blockage. 

15. Excursion Scenario 6.1 

15.1 Situation 

Scenario 6.1 was exactly the same as scenario 6 with one exception (figure 22). The first 

alternative path, the trail out of the finger area north of the original path, was not blocked. This 

constituted a scenario not quite as challenging as scenario 6. This scenario was an ad hoc 

addition to the experiment and was not run in RIVET.
37 

Figure 22. Scenario 6.1 description. 

15.2 Expectations 

The expectations were that (1) AM would encounter the blockage on the original path and 

possibly find the opening to the trail to the north, and (2) upon encountering the initial blockage, 

the DR and FCI would generate new plans to the nearby alternate trail to the north. 

15.3 Results 

Table 12. Scenario 6.1 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

6.1 AM only Field 109 1 742.09 6.57 6.57 6.78 1 circles

6.1 AM only Field 114 483.67 5.09 5.09 5.71 4 circles

6.1 DR Field 107 8 232.81 1.58 0.12 1.45 1.46 9.60 Complete

6.1 DR Field 112 7 247.39 1.66 0.03 0.61 1.62 9.15 Complete

6.1 FCI Field 108 1 254.90 2.04 0.29 1.75 8.74 Complete

6.1 FCI Field 113 1 233.31 1.34 0.02 1.32 10.60 Complete

15.4 Autonomous Mobility 

In the two field runs, the XUV repeatedly circled in the cul-de-sac and never came close enough 

to the unblocked trail in the north to see the opening. After circling three or more times, the run 

was e-stopped. 

15.5 Dynamic Replanner 

In the two field runs, the XUV sensed the blockage in the original path and quickly generated 

and executed a new global path through the alternate trail to the north. These runs spent 37% 

and 92% of the time in HMP at the cul-de-sac before generating the new global plan that worked.
38 

15.6 Field Cost Interface 

In the two field runs, the XUV sensed the blockage in the original path and quickly generated 

and executed a new path through the alternate trail to the north. This occurred quickly and 

seamlessly on both runs. 

16. Excursion Scenario 6.2 

16.1 Situation 

Scenario 6.2 was exactly the same as scenario 6. This scenario was an ad hoc addition to the 

experiment and was not run in RIVET. The weight for local/global costs in the FCI planner 

calculations was varied in two runs using the FCI planner only. 

16.2 Expectations 

Observing the performance of FCI in scenario 6 and noting the number of times the XUV circled 

the cul-de-sac before generating a path out of the cul-de-sac, the experimenters decided to run 

the same scenario but with a different ratio of between the local and global planner weights for 

FCI. The variable CellScale was reduced from 0.2 to 0.1 for the excursion, giving more weight 

to the local planner, which was less forgiving of the obvious brush in the immediate path and 

caused the XUV to more quickly generate a path out of the cul-de-sac. 

16.3 Results 

Table 13. Scenario 6.2 results. 

Scenario 

# Planner Source Run Plans Dist (m)

Total time 

(min)

eng_off 

(min)

Time HMP 

(min) ET (min)

avg spd 

kph Backups Outcome

6.2 FCI Field 110 1 860.47 7.90 7.90 6.53 1 Complete

6.2 FCI Field 111 1 446.21 4.31 0.47 3.84 6.97 2 E-stop

16.4 Field Cost Interface 

As expected, on both runs, the XUV exited the cul-de-sac area after circling twice near the 

blockages. One run was completed, and the second run was e-stopped when the XUV ran into 

undetected and unmarked water after exiting the cul-de-sac. 
39 

17. Effects of Varying CellScale 

FCI runs in scenarios 5 and 6 successfully solved the cul-de-sac after several “circles” of the 

cul-de-sac area. Several looks at the terrain were required in order to cause the perception system 

to recognize the local blockages and increase the local cost, enabling the FCI planner to then select 

a plan that took the XUV out of the cul-de-sac area. FCI runs circled the area an average of 4.2 

times when the CellScale was set to 0.2. Data from these runs are found in table 14. 

Table 14. Number of circles for FCI 

runs in scenarios 5 and 6. 

Run 

No. 

No. of 

Circles CellScale Source

21.1 3 0.2 Field 

23 3 0.2 Field 

25.2 8 0.2 Field 

28 3 0.2 Field 

76 5 0.2 Field 

79.1 2 0.2 Field 

82 4 0.2 Field 

85 8 0.2 Field 

88 2 0.2 Field 

Average No. of Circles = 4.2 

While not conclusive, this is evidence that CellScale is influential in the performance of the FCI 

planner and should be investigated using different levels and various terrain situations. 

18. Similarities and Differences Between RIVET and Field Runs 

18.1 RIVET 

RIVET was an invaluable tool in preparing for the assessment. It provided a test bed for the 

integrated planners, which would not have been available otherwise. It has been used to 

replicate and test bugs and other issues that are only present when running all the processes and 

sensors present in the XUV. It also provides a more controlled environment with generous 

display options in which multiple people can work together when trying to troubleshoot the 

integrated system. Lastly, it proved to be a valuable training tool that reduces the learning curve 

for operating the XUV and the different planners. In many of the scenarios, RIVET’s 
40 

representation was so good that the behavior of the XUV in the field matched the simulation 

even in small nuances and exercised some bugs that were hard to reproduce. Still, there are a 

number of areas in which there is room for improvement. 

18.2 Matched Runs 

As illustrated in figure 23, in some instances, RIVET and field runs matched very closely. 

Figure 23. Example of closely matching RIVET and field runs. 

Table 15 shows the extent to which the XUV tracks in field runs matched the XUV tracks in 

RIVET runs. Overall, about half (48%) of the field runs closely resembled a RIVET run. 

Table 15. Correlations between RIVET and field results. 

Planner Result 1 2 3 5 6 7

RIVET DNF DNF DNF DNF DNF DNF 8/15

Field match 1/1 (100%) 1/4 (25%) 2/2 (100%)2/2 (100%)1/1 (100%)1/5 (20%) (53%)

RIVET Complete Complete Complete Complete Complete Complete 14/28 33/68

Field match 2/5 (40%) 0/4 (0%) 0/4 (0%) 5/5 (100%)5/5 (100%)2/5 (40%) (50%) (48%)

RIVET DNF DNF DNF Complete Complete Complete 11/25

Field match 2/3 (66%) 1/4 (25%) 1/3 (33%) 2/5 (40%) 5/5 (100%)0/5 (0%) (44%)

AM

DR

FCI

Scenario
41 

18.3 Lack of Variability in RIVET 

For all scenarios and each planner, the three RIVET runs produced the same end result and 

almost identical tracks. There was no variability between runs. There was at least one field run 

for each experimental condition that was very similar to corresponding RIVET runs; however, 

for any given set of conditions, the lack of variability in RIVET is a significant limitation. The 

plots in figure 24 show the repeatability of RIVET runs. Ideally, in subsequent studies, there 

would be the ability to introduce degrees of variability into the terrain to elicit a distribution of 

behaviors. That distribution would then serve as a reference for observed performance 

evaluations. There is no guarantee that the specific lay down of simulated terrain for each 

scenario in this study was in any way the best lay down. Further, the capability to easily 

introduce variable terrain would likely enhance the developers’ opportunities to experience 

interesting planner behaviors in the lab, where the feedback loop in software development is 

more efficient. 

Figure 24. Examples of lack of variability in RIVET.
42 

18.4 E-stops 

There were no e-stops in RIVET. All RIVET runs ended in a completed run or in max backups. 

In the current implementation, HMP was allowed to back into unknown areas, resulting in some 

e-stops in the field but with no consequences in RIVET. An effort might be considered to elicit 

from program participants the anatomy of an e-stop and include that run outcome in RIVET. 

18.5 HMP 

During the DR runs, the XUV spent considerably less time in HMP in RIVET vs. field runs (see 

figure 25). HMP works flawlessly in RIVET and achieves much higher speeds than in the field 

runs. 

Figure 25. Time spent in HMP. 

18.6 Backups 

There appears to be no significant difference in the number of backups the XUV performed in 

RIVET vs. field runs. Reasons for the two spikes in the number of backups in figure 26 are not 

clear. In RIVET, scenario 3, FCI run 286 had 27 backups, run 287 had 3 backups, and run 288 

had 13 backups. In RIVET, scenario 6, AM run 261 had 5 backups, run 262 had 6 backups, and 

run 263 had 12 backups. An explanation would require additional investigation. 
43 

Figure 26. Number of XUV backups. 

18.7 Performance of FCI in Scenarios 5 and 6 

As discussed previously, FCI runs in scenario 5 and 6, which successfully solved the cul-de-sac 

or were halted after numerous circles of the cul-de-sac area, circled the area an average of 

4.1 times when CellScale was set to 0.2. Corresponding runs in RIVET are shown in table 16. 

Table 16. Number of FCI circles in 

scenarios 5 and 6 using RIVET. 

Run 

No. 

No. of 

Circles CellScale Source

12 2 0.2 RIVET

13 1 0.2 RIVET

215 1 0.2 RIVET

251 1 0.2 RIVET

254 1 0.2 RIVET

255 1 0.2 RIVET

Average No. of Circles = 1.2 

Although FCI solved scenarios 5 and 6 consistently in the field, this data shows a significant 

difference in the “efficiency” of FCI in those scenarios relative to the corresponding RIVET 

runs.
44 

18.8 Reasons for End of Mission 

Table 17 summarizes the reasons that an early end of mission was called on experimental runs 

that were not completed. 

Table 17. Reasons for early end of mission calls. 

Source Planner Reason Count 

Field AM only E-stop 3 

Field AM only Max backups 3 

Field AM only Stuck 1 

Field AM only Three circles 5 

Field DR E-stop 3 

Field DR E-stop HMP 5 

Field DR Max backups 1 

Field DR Stuck 1 

Field FCI E-stop 4 

Field FCI Max backups 9 

Field FCI Three circles 1 

RIVET AM only Max backups 18 

RIVET FCI Max backups 12 

Five runs were ended prematurely for DR runs when HMP backed the XUV into unknown areas 

that were brush or trees, causing the e-stop. This is a known condition for HMP and has been 

addressed for future implementation. 

FCI runs were ended nine times for max backups. Although plans generated by FCI take into 

account the kinematics of the XUV, this precludes the kinds of tight maneuvers possible under 

HMP control. It appears that integration of HMP into the FCI planner would significantly 

improve its performance. 

19. Implications for Operator Performance 

This field assessment did not assess operator performance or the need for operator interventions. 

However, there are potential implications for operator performance resulting from these kinds of 

planning algorithms.
45 

As UGV intelligence becomes greater and increasingly autonomous tactical behaviors are 

implemented, the role of the human operator will change. Planning algorithms, such as those 

described and assessed in this report, will allow the robot to execute plans and reach the end goal 

with less human intervention. The robot will have the capability to achieve the end goal with 

flexibility as to the specific route taken. The UGV will be able to pick and choose its route even 

as the actual terrain presents obstacles and challenges. The operator will have fewer direct 

control tasks and be called for fewer interventions, thus reducing operator workload. 

It is important, though, to consider other potential implications for operator performance. While 

there may be less direct control tasks required, the operator will still be required to periodically 

monitor and supervise the UGV progress. For some activities, such as the cul-de-sac problem 

(e.g., see scenario 6), the operator could see, at first, the UGV moving toward the goal and then, 

some time later, see the same UGV heading in the exact opposite direction. For the operator, this 

behavior may be inexplicable. 

A major need will be some way for the operator to understand UGV actions and determine if its 

behavior is appropriate or unreliable. What information is needed and how it is conveyed to the 

operator is an open question and should be considered as the technology develops. Considering 

and addressing the implications for operator performance will enable more successful interaction 

between humans and unmanned intelligent systems so that such systems can be used to their full 

capability in an operational environment. 

20. Conclusions 

Both DR and FCI are significant improvements over AM in improving the ability of the XUV to 

solve complex terrain blockages and to plan and execute alternate routes to a goal point. For a 

quick summary, the results are again presented in tables 18 and 19. Overcoming such challenges 

represents a major improvement in maneuvering in complex terrain and is an enabler for tactical 

behaviors. 

It is not fair to compare DR and FCI results because of the effects of HMP that are integrated 

with DR but not FCI. HMP seemed to enhance the capability of DR, allowing some intricate 

maneuvers to follow a new global route. At the same time, HMP was the direct cause of five 

e-stops because of a known limitation allowing an XUV back up into an area unknown to its 

map. FCI was stopped in nine runs because of max backups, meaning it could not maneuver out 

of the tight constraints around a blockage, a capability provided by HMP. 

Changes in FCI parameters that affect the weighting between local and global plans significantly 

affect the efficiency of FCI in solving terrain blockages (see excursion 6.2).
46 

Table 18. Goal achieved/runs attempted. 

Goal Achieved 

RIVET Field 

Scenario AM DR FCI AM DR FCI 

1 0/3 3/3 0/3 0/1 3/5 1/3 

2 0/3 3/3 0/3 3/4 3/4 2/4 

3 0/3 3/3 0/3 0/2 0/4 2/3 

4 0/3 3/3 0/3 — — — 

5 0/3 3/3 3/3 0/2 5/5 2/5 

6 0/3 3/3 3/3 0/1 5/5 5/5 

7 0/3 3/3 3/3 2/5 2/5 0/5 

Table 19. Challenges solved/challenges attempted. 

Challenges Solved 

 RIVET Field 

Scenario AM DR FCI AM DR FCI 

1 0/3 3/3 0/3 0/1 4/5 1/3 

2 0/3 3/3 0/3 3/4 3/4 3/4 

3 0/3 3/3 0/3 0/2 1/4 2/3 

4 0/3 3/3 0/3 — — — 

5 0/3 3/3 3/3 0/2 5/5 2/5 

6 0/3 3/3 3/3 0/1 5/5 5/5 

7 0/3 3/3 3/3 3/5 4/5 0/5 

Lack of variability of performance in RIVET limits its usefulness and its validity with respect to 

field runs toward building a complete reference distribution of expected behaviors. Still, 48% of 

the field runs are very similar to corresponding RIVET runs. Even without variability 

introduced, RIVET is invaluable in confirming/augmenting SME notional understanding of 

planner behavior in the scenario planning stages. 

DR and FCI greatly reduce the need for operator intervention to assist the XUV in overcoming 

an unexpected terrain blockage. On DR and FCI runs, no operator intervention was required. 

No measure of operator workload reduction was collected. 

For DR and FCI, locally sensed data was used to update or replace a priori data in both the local 

and global maps. Both planners were able to explore alternate routes through areas where 

a priori data did not exist. 

The one instance of the use of false a priori data (scenario 3) was inconclusive. One DR run 

attempted to use the false a priori trail in its plan, but the locally sensed data showed the area as 

impassible and alternate routes were generated (see field run 65).
47 

The scenarios used in this technology assessment to evaluate the performance of the planning 

algorithms offered significant challenges to the XUV. The results show that beneficial 

improvements have been made over the previous ability of the vehicle to take advantage of 

sensed data for recognizing a blockage and planning alternative paths to the goal. It is hoped that 

as improvements are made in sensor range and resolution (e.g., midrange sensing) and these 

capabilities are integrated into the planning hierarchy, the UGV will be able to maneuver more 

effectively. This should reduce the number of pitfalls encountered by the vehicle, such as 

entering a cul-de-sac or a blocked road. The value of continued research in sophisticated path 

planning algorithms is that the vehicle will, at some point, be able to learn from its environment 

and self-adjust the parameters required to accommodate scenarios that differ in terrain type and 

complexity. Although significant progress has been made in enabling an unmanned autonomous 

vehicle to use local and global planning to overcome a number of difficult scenarios, a 

considerable amount of work will be required to realize a system that can perform in a robust 

manner when faced with the challenges found in the various unstructured, dynamic environments 

that a UGV will undoubtedly encounter. 
48 

INTENTIONALLY LEFT BLANK. 
49

Appendix A. Graphics and Narrative of All Runs 

A.1 Scenario 1 

A.1.1 Autonomous Mobility Only 

The three robotic interactive visualization and exploitation technology (RIVET) runs with the 

Autonomous Mobility (AM)–only Planner resulted in identical behaviors. The experimental 

unmanned vehicle (XUV) moved down the trail, saw the blockage, and backed up three times; 

the run was then halted (figure A-1). 

Figure A-1. Run 238-AM-RIVET. 

One field run (run 33) with AM only was conducted and similarly ended after max backups. AM 

only was unable to find an alternate route (figure A-2). 

Figure A-2. Run 33-AM-Field.
50

A.1.2 Dynamic Replanner 

The three RIVET runs with the Dynamic Replanner (DR) resulted in identical behaviors. The 

XUV moved down the trail and saw the blockage. The High-Maneuverability Planner (HMP) 

then maneuvered the XUV back to the point where an opening existed to the southeast. The 

XUV then drove through the opening and discovered an unmarked trail to its left and parallel to 

the original plan, and followed that trail to the goal. In the RIVET runs, HMP was invoked 

99.7% of the time. All three RIVET runs achieved the goal. The orange triangles appearing in 

the figures of DR runs represent the location of the XUV when a new global plan to the goal is 

accepted by the XUV (figure A-3). Hence, many new plans are observed in cul-de-sac 

situations, and fewer new plans are observed in less congested environments where the XUV is 

moving easily toward the goal. 

Figure A-3. Run 231-DR-RIVET. 

Three of the five field runs achieved the goal. On runs 31.3 and 42 (figures A-4 and A-5), the 

XUV saw the blockage, returned to the opening on the southeast, discovered the unmarked trail 

to its left, and proceeded slowly down the unmarked trail under HMP control. The DR 

continuously generated plans that directed the XUV back to the original trail, beyond the 

blockage. However, the XUV was unable to find a clear path through the brush and trees, and 

eventually the DR gave a plan that followed the unmarked trail to the goal. 
51

Figure A-4. Run 31.1-DR-Field. 

Figure A-5. Run 42-DR-Field. 
52

On run 35 (figure A-6), the XUV followed a similar path but found a clear path back to the 

original trail and completed the run this way. 

Figure A-6. Run 35-DR-Field. 

Runs 38 and 43 (figures A-7 and A-8) ended in e-stops when the HMP backed the XUV into the 

dense brush, which was an unknown area. 

Figure A-7. Run 38-DR-Field 
53

Figure A-8. Run 43-DR-Field. 

Two of the five field runs closely resembled the RIVET runs. 

A.1.3 Field Cost Interface 

None of the three RIVET runs achieved the goal point. All Field Cost Interface (FCI) runs in 

RIVET saw the blockage and backed up three times; the runs were then halted. Run 226 (figure 

A-9) is typical. 

Figure A-9. Run 226-FCI-RIVET.
54

Field runs 34 and 39 (figures A-10 and A-11) were very similar to RIVET runs, stopping after 

three backups. 

Figure A-10. Run 34-FCI-Field. 

Figure A-11. Run 39-FCI-Field. 
55

On run 32 (figure A-12), the XUV backed up three consecutive times without moving forward. 

This combined 60-m backup enabled it to discover the opening to the southeast and the 

unmarked trail paralleling the original path to the goal. The XUV proceeded down the unmarked 

trail to the goal. 

Figure A-12. Run 32-FCI-Field. 

A.2 Scenario 2 

A.2.1 Autonomous Mobility Only 

The three RIVET runs with the AM-only planner resulted in identical behaviors (figure A-13). 

The XUV moved down the trail, saw the blockage, and backed up three times. The runs were 

ended after three consecutive backups. AM only was unable to find an alternate route. 

Figure A-13. Run 241-AM-RIVET.
56

Three of the four AM-only field runs (47, 51, and 52) were able to successfully, but 

unexpectedly, find an alternate route to the east of the blockage (figures A-14–A-16). This was 

probably the result of sparse enough brush in the area for AM to find a local path and move the 

XUV through to the goal. With each successful run through the brush, the next run became 

easier for the planner to find a path through the brush. 

Figure A-14. Run 47-AM-Field. 

Figure A-15. Run 51-AM-Field.
57

Figure A-16. Run 52-AM-Field. 

Run 57 (figure A-17) ended in an e-stop when the XUV backed too close to a high-mobility 

multipurpose wheeled vehicle (HMMWV) used for blockage. 

Figure A-17. Run 57-AM-Field. 

None of the field runs were similar to the RIVET runs. 

A.2.2 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors (figure A-18). The XUV 

moved down the trail and saw the blockage. HMP then maneuvered the XUV back to the point 

where an opening existed to a trail to the east. The XUV then followed the trail to the goal. In 

the RIVET runs, HMP was invoked 76%–86% of the time.
58

Figure A-18. Run 206-DR-RIVET. 

Three of the four field runs achieved the goal. On runs 48, 49, and 53 (figures A-19–A-21), the 

XUV saw the blockage, generated new plans to the east of the blockage, and found its way to the 

goal. No plans were generated on these runs that would have utilized the trail to the west, 

leading to speculation that the alternate trail was not in the a priori data as it was in RIVET. As 

with AM only, the more trips through the shrubs, the wider and easier the path became. 

Figure A-19. Run 48-DR-Field.
59

Figure A-20. Run 49-DR-Field. 

Figure A-21. Run 53-DR-Field. 
60

On run 56 (figure A-22), the XUV was e-stopped after two backups because it backed into dense 

shrubs. 

Figure A-22. Run 56-DR-Field. 

None of the field runs resembled the RIVET runs. 

A.2.3 Field Cost Interface 

None of the three RIVET runs achieved the goal point; all RIVET runs ended after three backups 

shortly after encountering the blockage (figure A-23). 

Figure A-23. Run 220-FCI-RIVET. 
61

Runs 50 and 54 (figures A-24 and A-25) achieved the goal. These runs were similar to the 

successful DR and AM-only runs because the XUV saw the blockage, moved off the trail to the 

east of the blockage through high shrubs, and found its way to the goal. As with previous runs, 

the more trips through the shrubs, the wider and easier the path became. 

Figure A-24. Run 50-FCI-Field. 

Figure A-25. Run 54-FCI-Field. 
62

Run 46.2 (figure A-26) was e-stopped because of max backups, and run 55.1 (figure A-27) was 

e-stopped because it backed too close to the HMMWV used in the blockage. 

Figure A-26. Run 46.2-FCI-Field. 

Figure A-27. Run 55.1-FCI-Field. 

One field run was similar to the three unsuccessful RIVET runs.
63

A.3 Scenario 3 

A.3.1 Autonomous Mobility Only 

The three RIVET runs with the AM-only planner resulted in identical behaviors (figure A-28). 

The XUV moved up the trail, saw the blockage, and backed up three times. The runs were ended 

after three consecutive backups. AM only was unable to find an alternate route. 

Figure A-28. Run 293-AM-RIVET. 

The two field runs with the AM-only planner (runs 63 and 64) resulted in behavior identical to 

the RIVET runs (figures A-29 and A-30). The XUV moved up the trail, saw the blockage, and 

backed up three times. The trail at the point of the blockage was steep and deeply rutted, making 

any maneuver difficult. The runs were ended after three consecutive backups. AM only was 

unable to find an alternate route. 

Figure A-29. Run 63-AM-Field.
64

Figure A-30. Run 64-AM-Field. 

A.3.2 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors. The XUV moved up the trail 

and saw the blockage. HMP then maneuvered the XUV back to the point where an opening 

existed to the west. DR then generated successive plans to move the XUV to the goal through an 

unmarked trail. The a priori road to the east was not explored. In the RIVET runs, HMP was 

invoked 57%–77% of the time. 

None of the four field runs achieved the goal. On runs 62 and 68 (figures A-31 and A-32), the 

XUV saw the blockage and generated new plans back to the point where an opening existed to 

the west. However, HMP was unable to maneuver the XUV back down the steep trail, and both 

runs were e-stopped when HMP backed into high brush. 

Figure A-31. Run 62-DR-Field.
65

Figure A-32. Run 68-DR-Field 

During run 72 (figure A-33), the XUV went through the barrels at the blockage and was 

e-stopped. 

Figure A-33. Run 72-DR-Field. 

On run 65 (figure A-34), the XUV maneuvered back to the base of the hill, explored the “false” 

a priori trail to the east, found it impassable, and generated plans to the west of the original plan 

toward the unmarked trail to the goal. However, the XUV failed to find the small opening in the 

brush for the unmarked trail and was e-stopped short of the goal. In the field runs, HMP was 

invoked 25%–85% of the time.
66

Figure A-34. Run 65-DR-Field. 

None of the field runs resembled the RIVET runs. 

A.3.3 Field Cost Interface 

The three RIVET runs with the FCI planner resulted in identical behaviors (figure A-35). The 

XUV moved up the trail, saw the blockage, and backed up three times until the run was halted 

for max backups. 

Figure A-35. Run 286-FCI-RIVET.
67

Two of the three FCI field runs achieved the goal. On runs 61 and 69 (figures A-36 and A-37), 

the XUV saw the blockage, backed to the base of the hill, and immediately explored the area to 

the west of the original plan, discovering the unmarked trail to the goal. 

Figure A-36. Run 61-FCI-Field. 

Figure A-37. Run 69-FCI-Field. 
68

In run 66 (figure A-38), the XUV moved up the trail, saw the blockage, and backed up six times 

until the run was halted for max backups. 

Figure A-38. Run 66-FCI-Field. 

A.4 Scenario 4 

A.4.1 Autonomous Mobility Only 

Scenario 4 was only run in RIVET. The three RIVET runs with the AM-only planner resulted in 

identical behaviors (figure A-39). The XUV moved up the trail, saw the blockage, and backed 

up three times. The runs were ended after three consecutive backups. AM only was unable to 

find an alternate route. 

Figure A-39. Run 283-AM-RIVET.
69

A.4.2 Dynamic Replanner 

The three RIVET runs with the DR resulted in identical behaviors (figure A-40). The XUV 

moved up the trail and saw the blockage. HMP then maneuvered the XUV back to the point 

where an opening existed to the west. DR then generated successive plans to move the XUV to 

the goal through an unmarked trail. 

Figure A-40. Run 280-DR-RIVET. 

A.4.3 Field Cost Interface 

The three RIVET runs with the FCI planner resulted in identical behaviors (figure A-41). The 

XUV moved up the trail, saw the blockage, and backed up three times until the run was halted 

for max backups. 

Figure A-41. Run 275-FCI-RIVET.
70

A.5 Scenario 5 

A.5.1 Autonomous Mobility Only 

All three RIVET runs ended on max backups immediately upon encountering the blockage on 

the initial planned path (figure A-42). AM only would see the blockage of the original path, 

back up, and be unable to find a plan through the blockage. 

Figure A-42. Run 234-AM-RIVET. 

In the two field runs (16.1 and 20) with the AM-only planner, the XUV circled repeatedly in the 

cul-de-sac, trying to find an acceptable way along the planned path (figures A-43 and A-44). 

After circling three or more times, the runs were e-stopped. 

Figure A-43. Run 16.1-AM-Field.
71

Figure A-44. Run 20-AM-Field. 

A.5.2 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route and saw that the path was 

blocked. It attempted to go through the alternate route nearby to the north but saw that it, too, 

was blocked. The XUV then replanned back east past the starting point, around the a priori trees 

and south toward the tank trail and a clear path to the goal (figure A-45). These runs spent 49%–

54% of the time in HMP at the cul-de-sac while generating and trying alternate plans. 

Figure A-45. Run 23-DR-RIVET.
72

The five field runs (figures A-46–A-50) resembled very closely the RIVET runs. The field runs 

tended to spend additional time and distance exploring alternate routes in the cul-de-sac, 

probably because of the highly varied vegetation on/around the alternate paths out of the cul-desac. These runs spent 28%–53% of the time in HMP at the cul-de-sac while generating and 

trying alternate plans. 

Figure A-46. Run 30-DR-Field. 

Figure A-47. Run 27-DR-Field. 
73

Figure A-48. Run 22-DR-Field.

Figure A-49. Run 19-DR-Field. 
74

Figure A-50. Run 18-DR-Field. 

A.5.3 Field Cost Interface 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, circled several times while attempting to find a way through the original and alternate 

trails, and then eventually replanned back past the starting point, around the a priori trees and 

south toward the tank trail and a clear path to the goal (figure A-51). 

Figure A-51. Run 12-FCI-RIVET. 
75

Two of the five field runs were successful in finding an alternate route. Runs 23 and 28 (figures 

A-52 and A-53) were successful in achieving the goal but were 100–200 m longer than the 

corresponding RIVET runs because these runs spent additional time and distance circling in the 

cul-de-sac and building up local terrain data before deciding to replan back past the starting 

point, around the a priori trees and south toward the tank trail and a clear path to the goal. 

Figure A-52. Run 23-FCI-Field. 

Figure A-53. Run 28-FCI-Field. 
76

Three of the runs (17, 25.2, and 21.1) saw the blockage and spent additional time and distance 

circling in the cul-de-sac, but FCI never generated a plan back toward the start point (figures 

A-54–A-56). The runs were ended after three or more circles. 

Figure A-54. Run 17-FCI-Field. 

Figure A-55. Run 25.2-FCI-Field. 
77

Figure A-56. Run 21.1-FCI-Field. 

Two of the five field runs resembled very closely the RIVET runs. 

A.6 Scenario 6 

A.6.1 Autonomous Mobility Only 

All three RIVET runs were ended on max backups after circling and repeatedly trying to go 

through the blocked area (figure A-57). 

Figure A-57. Run 261-AM-RIVET. 
78

In the one field run with AM only, the XUV circled repeatedly in the cul-de-sac, trying to find an 

acceptable way along the initial planned path (figure A-58). After circling three or more times, 

the run was stopped. 

Figure A-58. Run 78-AM-Field. 

A.6.2 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, attempted to go through the alternate route nearby to the north but saw that it also was 

blocked, then replanned back past the starting point and discovered a path through the trees south 

toward the tank trail and a clear path to the goal (figure A-59). These runs spent 40%–42% of 

the time in HMP at the cul-de-sac while generating and trying alternate plans. 

Figure A-59. Run 258-DR-RIVET.
79

The five field runs (figures A-60–A-64) resembled very closely the RIVET runs. The field runs 

tended to spend additional time and distance exploring alternate routes in the cul-de-sac, 

probably because of the highly varied vegetation on/around the alternate paths out of the cul-desac. These runs spent 31%–77% of the time in HMP at the cul-de-sac while generating and 

trying alternate plans. 

Figure A-60. Run 77.1-DR-Field. 

Figure A-61. Run 89-DR-Field. 
80

Figure A-62. Run 86-DR-Field. 

Figure A-63. Run 84-DR-Field. 
81

Figure A-64. Run 80-DR-Field. 

A.6.3 Field Cost Interface 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, circled several times while attempting to find a way through the original and alternate 

trails, and then eventually replanned back past the starting point and discovered a route through 

the trees south toward the tank trail and a clear path to the goal (figure A-65). 

Figure A-65. Run 251-FCI-RIVET. 
82

All five field runs (figures A-66–A-70) were successful in finding an alternate route similar to 

the runs in RIVET. These runs spent additional time and distance circling in the cul-de-sac, 

building up local terrain data before replanning back past the starting point and discovering a 

path through the trees south toward the tank trail and a clear path to the goal. 

Figure A-66. Run 88-FCI-Field. 

Figure A-67. Run 85-FCI-Field.
83

Figure A-68. Run 82-FCI-Field. 

Figure A-69. Run 79.1-FCI-RIVET.
84

Figure A-70. Run 76-FCI-Field. 

All five field runs resembled very closely the RIVET runs. 

A.7 Scenario 7 

A.7.1 Autonomous Mobility Only 

All three RIVET runs were successfully completed as the XUV sensed the blockage and turned 

around 180° to return to the place in the trail where an alternative trail existed to the goal (figure 

A-71). The RIVET-modeled foliage around the blockage allowed this maneuvering (this was not 

expected). 

Figure A-71. Run 248-AM-RIVET.
85

The field runs varied considerably. Run 8 (figure A-72) completely ignored the original path and 

skirted to the south of the cul-de-sac, directly to the goal. Run 4 (figure A-73) found the cul-desac but backed up three times and was then positioned in a location where it was able to find an 

open path along the trail to the west and went directly to the goal. Run 2 (figure A-74) was 

e-stopped at the blockage site, run 10 (figure A-75) was e-stopped when the XUV backed into a 

rock, and run 13 (figure A-76) was ended for max backups. 

Figure A-72. Run 8-AM-Field. 

Figure A-73. Run 4-AM-Field. 
86

Figure A-74. Run 2-AM-Field. 

Figure A-75. Run 10-AM-Field.
87

Figure A-76. Run 13-AM-Field. 

A.7.2 Dynamic Replanner 

In the three RIVET runs, the XUV proceeded along the planned route and saw that the path was 

blocked. HMP directed a 180° turn, and the XUV followed a new plan back the trail and around 

the southern side of the trees to the goal (figure A-77). 

Figure A-77. Run 212-DR-RIVET. 
88

In the field runs, after the blockage was encountered, HMP backed the XUV the entire distance 

out of the cul-de-sac on four of the five runs. Two of those runs ended in completions (figures 

A-78 and A-79). 

Figure A-78. Run 1-DR-Field. 

Figure A-79. Run 6-DR-Field. 
89

The remaining three runs were able to escape the cul-de-sac but were unable to complete the run. 

A suspected navigation problem ended run 7 (figure A-80), and an HMP problem ended run 11 

(figure A-81). Run 15 ended for max backups (figure A-82). 

Figure A-80. Run 7-DR-Field. 

Figure A-81. Run 11-DR-Field. 
90

Figure A-82. Run 15-DR-Field. 

A.7.3 Field Cost Interface 

In the three RIVET runs, the XUV proceeded along the planned route, saw that the path was 

blocked, circled for a 180° turn out of the cul-de-sac, and followed a new plan back the trail and 

around the southern side of the trees to the goal (figure A-83). 

Figure A-83. Run 224-FCI-RIVET.
91

In the five field runs, the XUV encountered the blockage and backed up three times, but not far 

enough to enable the XUV to see the unobstructed path to the goal below the tree line. All five 

runs ended with max backups (figures A-84–A-88). 

Figure A-84. Run 14-FCI-Field. 

Figure A-85. Run 9-FCI-Field.
92

Figure A-86. Run 5-FCI-Field. 

Figure A-87. Run 3-FCI-Field. 
93

Figure A-88. Run 12-FCI-Field. 

A.8 Excursion Scenario 1.1 

A.8.1 Situation 

Scenario 1.1 was the same as scenario 1 except that the NBC area was removed in order to see if 

the DR would plan to use the low-cost main tank trail to the west as an alternate route (figure 

A-89). 

Figure A-89. Scenario 1.1 description.
94

A.8.2 Dynamic Replanner 

On neither run did the DR plan a path that utilized the main tank trail to the west. Run 116 

(figure A-90 [right]) was typical of the DR runs 31.3 and 42, where the XUV saw the blockage, 

returned to the opening on the right, discovered the unmarked trail to the left, and proceeded 

slowly down the unmarked trail under HMP control. The DR generated plans that directed the 

XUV back to the original trail, beyond the blockage. However, the XUV was unable to find a 

clear path through the brush and trees, and eventually the DR gave a plan that followed the 

unmarked trail to the goal. On run 114 (figure A-90 [left]), the XUV was e-stopped when the 

HMP backed it into an unknown area of high brush near the blockage. 

Figure A-90. Scenario 1.1 runs with no NBC blockage. 
95

A.9 Excursion Scenario 6.1 

A.9.1 Autonomous Mobility Only 

In the two field runs, the XUV repeatedly circled in the cul-de-sac and never came close enough 

to the unblocked trail in the north to see the opening (figures A-91 and A-92). After circling 

three or more times, the run was e-stopped. 

Figure A-91. Run 113.9-AM-Field. 

Figure A-92. Run 108.9-AM-Field. 
96

A.9.2 Dynamic Replanner 

In the two field runs, the XUV sensed the blockage in the original path and quickly generated 

and executed a new path through the alternate trail to the north (figures A-93 and A-94). These 

runs spent 37% and 92% of the time in HMP at the cul-de-sac while generating and trying 

alternate plans. 

Figure A-93. Run 106.9-DR-Field. 

Figure A-94. Run 111.9-DR-Field. 
97

A.9.3 Field Cost Interface 

In the two field runs, the XUV sensed the blockage in the original path and quickly generated 

and executed a new path through the alternate trail to the north (figures A-95 and A-96). This 

occurred quickly and seamlessly on both runs. 

Figure A-95. Run 112.9-FCI-Field. 

Figure A-96. Run 107.9-FCI-Field. 
98

A.10 Excursion Scenario 6.2 

A.10.1 Field Cost Interface 

As expected, on both runs, the XUV exited the cul-de-sac area after circling twice near the 

blockages (figures A-97 and A-98). One run was completed and the second run was e-stopped 

when the XUV ran into undetected and unmarked water after exiting the cul-de-sac. 

Figure A-97. Run 110.9-FCI-Field. 

Figure A-98. Run 109.9-FCI-Field. 
99

Appendix B. Experimenter’s Observation of All Runs

 

This appendix appears in its original form, without editorial change. 
100

Source Run Run Scenario # Planner Outcome Reason RC 

Comments 

JP Comments MC Comments SH Comments 

Field 1 1 7 DR Complete Great run. HMP plan on red in 

the beginning 

 Start backup out of road – HMP Backed o

toward gocul-de sa

Field 2 2 7 AM only DNF E_stop e-stopped before detecting 

barrels. went around and into bushes. E-stopped. 

 Changed backup to 10,20,30m. 

Rerun. Backup 10, 20 (almost to road). Backup – starts to left. Estop – going where it can’t go. End of mission called by Test Director (TD) 

Turn up tRe-ared. forward away out tgoal. Loostart poin

Field 3 3 7 FCI DNF Stuck-max 

backups 

 max backups After turn, stop to look around – 

continued forward. Backkup, then goes forward. Backup into intersection, continues forward. Stuck 

– end of mission by TD 

BU from Again shpath. Stuc

Field 4 4 7 AM only Complete admin e-stop twice for safety. 

Plan complete 

 Backup, backup, take same road to 

right. Estop (admin) to make sure its clear, rearm. Backup into interaction. Turn left onto left path. Turn to right – Estop (admin), Rearm, resume. Complete 

Estop. RePositioneto road. C

Field 5 5 7 FCI DNF Stuck-max 

backups 

 max backups Backup, backup into intersection. 

Goes back up to the right. Stuck – Max Backups 

Up to barenough, bbarrels. S

Field 6 6 7 DR Complete admin e-stop. Plan complete “R:26” on status bar on displays 

showing replanning using DR. “U: Need plan” on status bar shows requesting plan from HMP. Estop (Admin) to get “eyes on” XUV. Complete [Note: after run, Mike removes rock from middle of road] 

5 m from down and way dowstop clearpath. Com

Field 7 7 7 DR DNF Nav problem E-stopped. Saw path blocked 

ahead (wrong) and turned left (bushes) for alternative. Possibly issues with IRU calibration 

 HMP – making K turn. Estop – 

because of terrain. Backup. End of mission – called because could not assess terrain in front of bot (called by TD). Turned to left off road because road looked completely blocked due to “position error” 

To barreldown. Charound. OTurned to Turned lebrush. (SNAV) 

Field 8 8 7 AM only Complete Didn't turn right!!! no idea why. 

Possibly issues with IRU calibration 

 Went straight ahead then stopped 

when within 20m of endpoint (did not make right turn)Complete [showed that the bot was within path tolerance of 30m so did not show “out of path” error] [could be that going fast and debating whether to go left or right; maybe going too fast to make right turn] 

Overshot complete

Field 9 9 7 FCI DNF Stuck-max 

backups 

 Engine stalled. Max backups Stalled coming up hill. Backup 10, 

20, 30. Operator Max Backups – Stuck – End of mission [at intersection, RCI couldn’t find a path to the left] 

Max back

Field 10 10 7 AM only DNF E_stop Estop-backed 

into big rock 

backup 3 times and was stuck Saw barrels, backed up, went in 

toward barrels again, backed up, EStop (backing into rock), Stuck, E.O.M. 

Field 11 11 7 DR DNF E_stop Estop-HMP 

stuck on 

obstacles 

knocked over 3 barrels on hmp.11:38 End of mission hmp could not plan off of obstacle 

Saw barrels, backup, backup, HMP dance, k-turn, accidental e-stop, XUV is clear of brush but hesitating, 

backup, came out of trail, turned right towards alternative path but overshot the turn, accidental e-stop, HMP planning for long time, HMP cannot find path (XUV is "sitting" on obstacles), E.O.M. 

 

Field 12 12 7 FCI DNF Stuck-max 

backups 

Stuck-max 

backups 

backed up 3 times and was stuck Stalled going up hill, backup, backup, 

backup, Stuck (Max backups), E.O.M. 
101

Field 13 13 7 AM only DNF Stuck-max 

backups 

Stuck-max 

backups 

stuck max backup Backup, Backup, Backup, accidental 

e-stop, max backups, E.O.M. 

Field 14 14 7 FCI DNF Stuck-max 

backups 

Stuck-max 

backups 

3 backups stuck Accidental e-stop, backup, backup, 

backup, backup, backup, Stuck, 

E.O.M. 

Field 15 15 7 DR DNF Stuck-max 

backups 

Stuck-max 

backups, filled 

in road path 

For some reason the map on idisp got blocked so the bot could not plan localy to the goal. stuck maxbackups 

Saw barrels, HMP turned XUV around, backup, admin stop, came out of trail, overshot slightly on turn, HMP maneuvering to take alternative, accidental e-stop, backup, backup, backup, HMP maneuvered away from path towards our location, HMP backed up and oriented XUV to face alternative path, started to go, Stuck, E.O.M. 

Field 16 16 5 AM only DNF E_stop nicely drove into bushes. Estopped Estop when went through middle pathway. Added more blockage and 

rerun 

Closed on Turned nEstop. 

Field 16.1 216 5 AM only DNF E_stop placed HMWVV into bushes. 

run into bushes. E-stoped. killed log. Restarted log. Continuation of Run16a. 11:11 E-stopped. Unable to find way out. 

 Estop (admin) to get truck out of the 

way. Estop – heading into bushes on left. Stop after 3-circle rule 

Stopped aCirce. Stoand we st

Field 17 17 5 FCI DNF 11:17 bot stuck after one 

backup. admin e-stop. seing things on the road. went exploring... e-stopped. Possibly issues with IRU calibration. engine not sounding right. Estop. continue from here. didn't find alternative goal. Stopped 

 Approached middle path, stalled, 

“Bot Stuck”, resumed. Estop for safety (too near HMMWV). Estop for safety (too near Robert). Resume. Estop- stalled – stuck on hill and ditch. Resume – circling around many times. Suspend – End of mission by TD 

Multiple stopped i

Field 18 18 5 DR Complete 64 plans, looks 

like lost NAV, 

got past the 

short end point

11:54 e-stopped before puddle. Teleop around. Continue. 11:59 AM sign on the road. E-stop to pull it out 

 Poked around and then headed out. 

Teleop around puddle. [dark blue dotted line appears every time there is a new C2 plan] 

SuccessfuRouted argoal. 

Field 19 19 5 DR Complete goto haven't moved. 1:38 admin 

e-stop. 1:47 redraw puddle. 1:53 mission complete 

 Estop (admin) for safety near 

HMMWV. Resume. Poked around and then went back out of finger. Estop for water hole 

Charged and loope

Field 20 20 5 AM only DNF 2:10 pm accidental e-stop. 

2:13PM unable to get out 

 Accidental Estop. Poking around. 

End of mission – 3 circle rule (AM stuck in cul-de-sac) 

Many cir

Field 21 21 5 FCI DNF Not recovering well from Estop. 

Thinking the NBC location is expanding and screwing up planning 

Circled mof the foopuddle (bBrought i

Field 21.1 221 5 FCI DNF E_stop e-stopped for puddle. unable to 

resume 

 Circled a

the point. 

Field 22 22 5 DR Complete removed puddle from SMI. 

Placed barrels in front of puddle. accidental e-stop. Complete 

 Accidental Estop. Stalled going up 

hill. Complete [only way operator knows what mode is by the replan] 

Quickly sblockage 

Field 23 23 5 FCI Complete stopped for puddle. going 

slowly (2.5m/s). Plan complete 

 JP walked in front of XUV to create 

obstacle so it won’t go thru bigger puddle 

Circled ofront to csouth and found its 

Field 25 25 5 FCI DNF E_stop estop-water had to estop the bot because it 

was heading to a huge puddle 

Drove over cones in center area, backup, drove into mud puddle, backup, circled round, drove into cones in center area, backup, drove out of cul-de-sac, turned around and came back (reportedly Prim data is scrolling off of map and causing this bungie cord effect), E.O.M. 

Field 25.1 225 5 FCI DNF Circled circled had to estop but because of 

puddle again. idisp capture failed-had to restart idisp 

Backup, headed into mud over cone, E-stop, E.O.M. (moved barrels (3) to block puddle) 
102

Field 25.2 226 5 FCI DNF Circled circled, exited 

3x, came back

fci just oscillated back and for and would not leave culdesac 

Drove over cones in center area, backup, drove into mud puddle, backup, circled round, drove into cones in center area, backup, drove out of cul-de-sac, turned around and came back (reportedly Prim data is scrolling off of map and causing this bungie cord effect), E.O.M. 

Field 27 27 5 DR Complete good at 

culdesac, 

exitted 

succussfully reached road ended mission due to lack of range time 

Looked right and backed up, looked left, backup, looked in the center area, backup 

Field 28 28 5 FCI Complete got to road, 

circled, EOM 

obstHeight=0.5. successfully turned around. Once it got to road, headed in the wrong direction, turned around and saw chase vehicle. e-stopped 

0.5m obstacle height, XUV inspected blocked path, circled round a couple of times, chose alternative path out of sul-de-sac 

 Three cir

out , turncircle at t

Field 30 30 5 DR Complete hmp on obststacle. paused as it 

entered the road back. mission complete 

XUV detected obstacles, assumed full blockage, replanned, maneuvered on alternative path out of cul-de-sac 

 Forward 

probing vHMP and 

Field 31 234 1 DR DNF could not find its way. Took right turn immediately, admin 

stop, backup 5m, 10m, 15m, E.O.M. declared - recommended to reduce barrel height (obstacle height = 1.5m)

Field 31.1 233 1 DR DNF HMP backed 

into brush 

reduced barrel height and started closer to the start point. bot backing up into unknown in hmp. 10:35 stopped 

XUV went further down path, saw barrels, then invoked HMP, drove backwrds through brush along new path, E-stop, E.O.M. 

 Went into 

moving it 

Field 31.2 231 1 DR DNF new barrel 

locations+10m: 

saw alt rt as 

blocked 

starting farther back and barrles moved. failed to find way out 

Saw obstacles, stopped adjacent to opening, hesitated because HMP could not find valid path, backup 5m, turned right at opening, took left at next opening, backed up, turned right and drove into brush (searching for path), E.O.M. 

 

Field 31.3 232 1 DR Complete new barrel 

locations+10m

obst height 1.5. complete: but goal moved before ending and had to pause 

E-stop, backup, HMP backed XUV down alternative, backed over tall brush and small trees, turned around, went forward over small tree, round road, maneuvered to goal 

 Backed in

Eventuallpath and 

Field 32 32 1 FCI Complete 3 Bus for 60m, 

found alt rt, 

direct to goal 

obst height=1.5, coverheight=3.0. plan complete 

Obstacle height = 0.5m, saw barrels, veered right, backup 10m, backup 20m, backup 30m, cut corner to alternative path, E-stop, maneuvered 

to goal 

 10 m BU

Admin e-(EffectiveDid nt do 

Field 33 33 1 AM only DNF Stuck-max 

backups 

ran thru barrels 

3x, 3BUs, 

stuck on log 

changing obst height to 1.4. stuck backing up 

Obstacle height = 1.4m, saw blockage, backup, dove left, backup, drove forward, backup into trees, Stuck (physically), E.O.M. 

 BU got p

m. 

Field 34 34 1 FCI DNF Stuck-max 

backups 

backed up 

~40m, max 

backups 

10:34 stuck max backups Saw barrels, backup, dove left, 

backup 20m, backup 30m, backed into trees, Stuck (Max backups), E.O.M. 

 Stuck ma

m total. 

Field 35 35 1 DR Complete seoCoverHeight=3.0. 1:35 

complete 

Saw barrels, backed up, HMP maneuvered XUV to path on right, XUV followed path and took left down alternative, XUV is hunting and pecking to back to original path (reportedly the global planner did not consider the barrels to be a full blockage), XUV went through brush and went behind barrels to join the road, admin stop, maneuvered to goal [Planning to get to road beyond the barrels.] 

 Complete
103

Field 38 38 1 DR DNF Stuck-max 

backups 

found alt rt, 

HMP backed 

into brush 

1:50 bot stuck. AM didn's see a path forward 

Saw barrels, stopped, backed up, turned right on alternative, turned left to go down path (looking to get back on original path), XUV backed up (but was to right off path)backed into small trees, E-stop, Resume, Stuck, 

E.O.M. 

 Backed in

failure. In

Field 39 39 1 FCI DNF Stuck-max 

backups 

 stuck max backups Saw barrels, stopped, trying to go 

around to left, E-Stop, Resume, backup, backup 20m, backup 30m, Stuck (Max backups), E.O.M. 

 Incomple

Field 42 42 1 DR Complete tried to return 

to left rd, 

continued to 

right to goal 

11:01 plan complete Saw barrels, stopped, HMP backup 

and k-turn, came back along original path, turned left on alternative path, left again, cut left into brush, backup, backup, Admin stop (to move truck), Resume, backup, backup, took left 

shorcut again, backup (near trail), backup,k took path o right to road, 

 Complete 

Field 43 43 1 DR DNF E_stop HMP backed 

into brush on 

k-turn at 

barrels 

hmp vehicle on red. ACC2 planning through obstacles...?? backup path not safe 

Saw barrels, backup, HMP backed into unknown terrain, E-stop, Resume, backing into unknown terrain again, E.O.M. 

 Incomple

Field 46 46 2 FCI DNF broke steering lost steering hit rock and broke steering XUV bounced down path, saw 

blockage, backed up in a zig zag fashion, backed into brush, backed in a circle, E-Stop, E.O.M. 

 

Field 46.1 146 2 FCI DNF obst height=1.4. coverheight=3. 

speed 3.5 m/s. bot backed up from the begginning 

XUV immediately backed up, XUV hit barrels 

Field 46.2 147 2 FCI DNF Stuck-max 

backups 

 AM stuck becau se of e-stop. 

Stuck 

XUV saw the blockage (but bumped barrels), backup 10m, backup 20m, XUV found alternative path, E-Stop (went right into brush), Resume, backup 10m, Admin stop (to allow truck to clear), Resume, Stuck (Operator Stuck), E.O.M. 

Field 47 47 2 AM only Complete knocked down two barrels. plan 

complete 

XUV saw blockage but bumped barrels as it stopped, backup, took right path, backup, E-stop (rock), achieved goal 

Field 48 48 2 DR Complete not running hmp. starting it 

mid-run 

XUV saw barrels and bumped the first as it stopped, backup 10m, backup 20m, took right alternative path, found goal 

Field 49 49 2 DR Complete 11:24 plan complete XUV ran into 2 barrels, E-stop, 

Resume, Backup, HMP looking for path, trying to go left, nosing into trees, backup, pushing trees, backup, took right path, got near goal and tried to cut corner, HMP backup and dancing, found goal, E.O.M. 

Field 50 50 2 FCI Complete hit barrels again. fw000.sav. 

plan complete! 

XUV charged barrels, E-stop, Resume, backup 10m, backup 20m, turn right on alternative, turned left into brush, backup, backup 20m, continues along alternative path, went across main raod toward helipad, backup 10m, backup 20m, backup 30m, maneuvered to goal 

Field 51 51 2 AM only Complete hit barrels again. e-stop. plan 

complete (too early?) 

XUV bumped barrels, backup 10m, backup 20m, took alternative path to right, backup 30m, admin stop, Resume, admin stop, Resume, cutting left, backup 10m, cut right to road, then to goal 

Field 52 52 2 AM only Complete plan complete (after adjusting 

steeting ... see run53e) 

AM saw barrels and turned to right w/out entering cul-de-sac, backup 10m, maneuvered to goal 
104

Field 53 53 2 DR Complete plan complete Immediately took right turn backup, 

Admin stop, proceeded to goal 

Field 53.9 153 2 DR Complete after adjusting steering, laser 

pan. obst height 1.3. seoAngleOffsetPan=-0.0667. plan complete. HMP not working well 

XUV saw barrels and stopped short, backup 5m, backup 2m, backup 2m, 

backup 3m, backup 5m, took alternative path to the right, E-stop (Rock), Resume, reached goal 

Field 54 54 2 FCI Complete plan complete Saw barrels and immediately headed 

right, stopped, resumed, found goal 

Field 55 155 2 FCI DNF placing HMWWV in path to 

right. found gap. 

Saw barrels and stopped in cul-desac, backup 10m, backup 20m, Estop (backed too close to HMMMWV), backup 30m, drove into cul-de-sac and stopped short, backup 10m, E-stop (tried to cut in front of HMMWV, E.O.M. Place HMMWV to block right alterntive path. Stopped in cul-de-sac, backup, backup, headed toward left path, backed up into trees, E-stop, Resume, backup into trees, E-stop, Max backups, Resume, forward toward cul-de-sac, backup into trees, E-stop, E.O.M. 

Field 56 56 2 DR DNF backed up into unknown E-stop (headed for HMMWV), 

Resume, entered cul-de-sac, backup, HMP dancing, backing up into unknown, Admin stop, Resume, backing into trees again, E.O.M. 

Field 57 57 2 AM only DNF too close to HMMWV E-stop (headed for HMMWV), 

Resume, entered cul-de-sac, E-stop (charging barrels), backup towards HMMWV (E-stop), Resume, backing close to HMMWV, E-stop, E.O.M. 

Field 61 61 3 FCI Complete 3 BUs and 

around to right

obst height 1.5. cover height 3.0. original prior data. complete!!! 

XUV went up hill, saw barrels, backup, backup 20m, E-stop (headed for truck), found alternative path, drove to goal 

 FCI on 3r

path to ri

Field 62 62 3 DR DNF did not see 1 

barrel, went 

into weeds, 

HMP problem

hmp backed into bush. bot not moving. canceled mission 

XUV drove up hill and saw an opening in left most barrel, backup, HMP tried to execute k-turn, XUV trying to go around barrels, HMP backed XUV into brush, E.O.M 

 Right up 

Stopped. up into wweird mo

Field 63 63 3 AM only DNF Stuck-max 

backups 

3 BUs, stuck 2:55pm stuck Stopped at barrels, backup 10m 

backup 20m, backup 30m, looked right and went up hill again, Max backups, E.O.M. 

 AM to ba

Could habut did no

Field 64 64 3 AM only DNF Stuck-max 

backups 

3 BUs, stuck stuck b/c engine stalled. reexecuting. 3:02 pm stuck Backup 10m, stuck, backup 10m, backup 20m, backup 30m, Stuck 

(Max backups), E.O.M. 

 All the w

(actually then 30. S

Field 65 65 3 DR DNF E_stop tried apriori rd 

to left, crossed 

trail, couldn't 

find alt rt 

3:20pm hmp not moving. restart hmp. 3:24 vehicle started exploring too far from path. estopped. 

Engine stalled, detected partial blockage, backup, HMP backed down hill, turned left and took false a priori road, admin E-stop, XUV sees partial blockage in road, turned round, drive over to alternative path (on right), backup, Stuck in HMP mode, killed HMP, Resume 

 Stopped r

straight sOscillate Look for apriori blnorth. Bapath. Expthick. 

Field 66 66 3 FCI DNF Stuck-max 

backups 

3 BUs, stuck 3:35pm stuck (max backups) Saw barrels, stopped just shy, 

backup, accidental e-stop, backup 20m, backup 10m, backup 20m, backup 30m, Stuck (Max backups), E.O.M. 

 All the w

10, 20 m. 30 m. Ma

Field 68 68 3 DR DNF E_stop backed into 

weeds on left, 

HMP problem, 

backed into 

tree 

stuck into tree Saw barrels, backup, HMP kturn/backup (filling in blockage), 

backup (filling in more of blockage), HMP is stuck (won't move), backing into trees, E-stop, E.O.M. 

 All the w

back up pForward HMP Bacmission. 
105

Field 69 69 3 FCI Complete Stuck-max 

backups 

3 BUs and 

around to right

stuck max backups. almost cleared. resumed. plan complete!!! 

Saw barrels, backup 10m, backup 20m, backup 30m, turned right on alternative path, Stuck (Max backups), Resume, found alternative and drove to goal 

 Back up f

Stuck mahill. Overcase. 

Field 72 72 3 DR DNF E_stop ran thru barrel hit barrels and kept moving XUV climbed hill, hesitated (but 

didn't stall), continued through barrels, E-stop, E.O.M. 

 Right up 

them. EnLADAR barrels. 

Field 76 76 6 FCI Complete Estop – cause? Complete mission 

[***Note that run number was incorrect wrt the original run sheet, but went with the run #s listed here] 

Found its 

Field 77 77 6 DR DNF E_stop Plgr fill changed. found opening 

through the bushes 

 Estop – into path – rearm to see if it 

would back up, it didn’t. End of run by TD. 

Bot found trail. Red

Field 77.1 277 6 DR Complete safety e-stop. Not enough 

blockage. hmp didn't move for a while . e-stopped - going through ditch 

 Complete. Estop – tried to go thru 

path at right. Estop – stuck Several other Estops. End of mission by TD – seen the most interesting part of the run 

Finally gstuck in wof cul-dealmost. C

Field 78 78 6 AM only DNF stopped. going in circles 3-circle rule. End of mission called 

by TD 

Many loo

Field 79 79 6 FCI DNF E_stop e-stopped because of large 

puddle 

 Ran circles. Headed out of finger, but 

then turned around again for a big circle. End of mission called by TD 

Run to w

Field 79.1 279 6 FCI Complete seoMinObstHeightFast/Slow=0.

5. got out of cul-de-sac... maybe faster??. 11:02 stopped because of puddle. drove nicely through narrow path 

 Reduce obstacle height to 0.5m. (on 

previous runs, height has been 0.75) 

Field 80 80 6 DR Complete obstHeight back to 0.75. 11:33 

back-forth w/o moving. 11:42 plan complete 

 Obstacle height at 0.75m. Complete Complete 

Field 82 82 6 FCI Complete obstHeight 0.5. 12:02 plan 

complete 

 Inconclusive. Obstacle height 0.5m. 3 

times around 

Complete 

Field 84 84 6 DR Complete 2:01 global path not blocked. 

2:19 goto no plan message but everything fine. 2:20 plan complete 

E-stop to prevent from heading over bank, E-stop (went over barrels) - walked in front of XUV to create obstacle, hit a branch-barrel combo, stopped, replanned out of culd-de-sac

 More lost 

in front aGot out othrough t

Field 85 85 6 FCI Complete reduced prior data. obst height 

0.5. 10:12 AM e-stopped before puddle. Otherwise good run 

0.5m obstacle height, XUV made several turns, went away from blockage, charged safety truck, made some more passes, assumed full blockage, took alternative path out of cul-de-sac 

 Four circ

Made it oroad. Com

Field 86 86 6 DR Complete obstHeight=0.75. hmp back and 

forth w/o moving. turned onto obstacle grade... need to check!!! 

XUV looked at obstacles, made one pass, then turned around and maneuvered out of cul-de-sac 

 Charge b

a few timpath. Tur road. 

Field 88 88 6 FCI Complete complete until before puddle. 

unable to find idisp capture. 

0.5m obstacle height, XUV took one pass by blockage, turned and took high path around safety truck, took first rght turn (shortcut) 

 Two circl

behind tru

Field 89 89 6 DR Complete obst height 0.75. e-stopped 

before cutting corner into density grade 

XUV looked at blockage, probed a little, HMP turned XUV around, maneuvered out of cul-de-sac via shortcut 

 Went to b

plan. Bacand gone

Field 107 106 6.1 DR Complete found 

unblocked 

upper road 

North road open. stopped (chase could not follow) bot found north road 

Drove directly to and up the upper trail 

 Upper ro

quickly. G

Field 108 107 6.1 FCI Complete found 

unblocked 

upper road 

North road open. set obst height 0.5. stopped(chase could not follow) bot found north road 2:53 

Drove directly to and up the upper trail 

 Complete 
106

Field 109 108 6.1 AM only DNF Circled circled, did not 

find unblocked 

upper road 

North road open. bot circled but did not found road. stop 15:08 

Drove directly to barrels and bumped one, stopped, drove in circles repeatedly, E.O.M. declared 

 AM did n

Field 110 109 6.2 FCI Complete out of culdesac 

but stuck later

egofccellscale=0.1. The bot went all the way around instead of NS trail. 3:33 stopped bot going to pond. bot saw edge of road as obstacle 

Drove to cul-de-sac, looked at abarrles, circled and looked at HMMWVs, took path out of finger, chose long route (because of cost of hard right turn?) 

 Got out o

circle or Around thback in. E0.2 weigh

Field 111 110 6.2 FCI DNF E_stop out of culdesac 

but stuck later

egofcCellScale=0.1. bot tried to take the closest NS trail but turned around and started heading to a puddle.15:52-stopped due to puddle 

Circled in front of obstacles a few times, looked over bank, stopped, bacup 10m, took path out of cul-desac, took first right turn (short cut), turned around, came back out of short cut, E-stop (headed for ditch) 

 Circled a

Took firswater trou0.2. Uppe

Field 112 111 6.1 DR Complete found 

unblocked 

upper road 

removing blockage from upper trail. almost hit tree. Stopped after committing to the upper trail 

Entered cul-de-sac, saw blockage, saw opening in upper trail and took it

 Came dow

Path to upup to it. G

Field 113 112 6.1 FCI Complete found 

unblocked 

upper road 

obst height 0.5. good run. Entered cul-de-sac, paused 65m from 

blockage, was opening in upper trail and took it 

 FCI came 

Quickly rhole to upupper trai

Field 114 113 6.1 AM only DNF Circled circled, did not 

find unblocked 

upper road 

obst height 0.75. circling around XUV probed the blockage, circled, 

repeated this behavior three times, E.O.M. 

 Max circ

Field 115 114 1.1 DR DNF E_stop No NBC, 

backed into 

weeds on rt, 

HMP problem

removing NBC 11:43 stuck backing up 

Barrel fell over, E-Stop, reposition barrel, Resume, dove right, trying turn around, backed into opening, Estop, Resume, backup, HMP backing into unknown terrain, E.O.M. 

 HMP fail

Field 116 115 1.1 DR Complete No NBC, 

found trail to 

right, not main 

road 

repeat from 114. hmp not moving restarting. plan complete 

Stopped, backup (early), backup, took alternative path to right, took first left, dove left into brush, HMP stuck mode, killed HMP, backup, turned right, maneuvered down alternative path, found goal 

 Complete 
107 

List of Symbols, Abbreviations, and Acronyms 

AM Autonomous Mobility 

ARL U.S. Army Research Laboratory 

C2 Command and Control 

DR Dynamic Replanner 

e-stop emergency stop 

FCI Field Cost Interface 

FTIG Fort Indiantown Gap 

HMP High-Maneuverability Planner 

NBC Nuclear, Biological, Chemical 

RCTA Robotics Collaborative Technology Alliance 

RIVET robotic interactive visualization and exploitation technology 

TRL technology readiness level 

TVMA-B Tactical Vehicle Maneuver Area-Bravo 

UGV unmanned ground vehicle 

XUV experimental unmanned vehicle 
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