Flexible Framework for
Quantitative Reachability
Analysis
November 1, 20191

Agenda Background Existing solutions
 REACH Results Metrics
 Framework
 Plugins Example
 Future work
 Relevance2

Background Industrial robotic systems designed to perform specified task(s)– Opposed to some robotics applications where new use-cases areresearched on existing hardware

 Considerations for robotic system design– Workspace size
– Workspace constraints– Workpiece geometry– Robot size
– Robot configuration

 How to evaluate concepts to objectively?
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Background What do we want?
– A design that:
1. Can reach an acceptable area on a workpiece with a given tool(s)2. Has the most flexibility for accommodating new parts/processes and/orchanges to the environment
3. Stays as far away from collision with the environment as possible– To understand:• How changes to system configuration affect the goals defined above• How the robot system will reach desired points

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SwRI Examples
 Laser De-paint Robot– Must reach ~90% of area on midsize aircraft (e.g. Boeing 737,Airbus A320)– Proposed configuration: 11+ DOF• 8+ DOF manipulator• 3 DOF mobile base5

SwRI Examples
 Military Aircraft Maintenance
Robot– Must service ~50% of area on
C-17 aircraft– Tool Z-orientation free
– Proposed configuration: 10+DOF• 7+ DOF manipulator• 3 DOF mobile base6

Existing Solutions
 Brute force– CAD environment– Offline programming software– 3D printed models– Hard , time-consuming, and expensive

 Smarter Approach– Automated robot base placement• Siemens Process Simulate– Insufficient for high-DOF systems andmobile robots– Inverse reachability• ROS-I Reuleaux package– Lacks focus on the workpiece

Adapted from [1]7

REACH REACH– https://github.com/ros-industrial/reach Core Process– Generate desired reach points on aworkpiece– Solve inverse kinematics at each point– Evaluate the reachability at each point– Maximize the reachability values– Report and visualize the results

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REACH Maximize the reachability values– Infinite number of IK solutions forhigh-DOF systems– Gradient-based IK solver– Initial IK solution generally produces
low score (if solution is even found)– At each target• Use neighbors as IK seed states• Re-solve IK at target• Re-evaluate reachability at target– Iterate until reachability stops
improving9

Framework
 Plugin-based architecture– Environment/inverse kinematics
interface– Reachability evaluation criteria– Display interface

 Provides flexibility for different backends
 User-specifiable via YAML file10

Results Metrics
 Results Metrics– Percentage of targets reached– Total reachability score of all points– “Potential” total reachability score• What would the score be if therobot reached every target?
• Total score / percentage reached– Average number of reachable
neighbors

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Plugins
 Inverse Kinematics– 6-DOF constraint– Discretize about Z-axis
 Evaluation criteria– Manipulability• How easily the robot can move in any direction
from a given pose

– Nearest distance from collision– Distance from joint configuration– Combination of metrics (sum,product, etc.)
Adapted from [2]12Plugins
 Display plugin– Interactive markers at targets• Display robot state• Re-solve IK• Show seed state– Comparison between configurations– Results heat map13

Example Laser De-paint Robot– C-17 aircraft– Results• Reach percentage: 93.6%• Score: 328,378• Normalized score: 350,832

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Example Decide between several designconcepts– Robot mounted on gantry– Multiple workpieces– Spherical wrist vs. offset wrist robot

Adapted from [2] Use reach study data to narrow down
concepts– % reachable: R1 ≈ R2
– Raw score: R2 > R1
– Potential score: R2 >> R1– Use Design1, Robot 2

Design Work-piece

D1
D2
D3

Object 1
Object 2
Object 1
Object 2
Object 1Object 2

Robot 1

Robot 2%
Raw Potential%
Raw Potential
Reached Score Score Reached Score Score
91.90%
73.60%
92.70%
73.20%
74.80%
57.40%

338.5
290.5
357.5
287.2
301.8
271.1

368.3
394.7
385.7
392.4
403.5
472.3

91.20%
70.20%

351.9
345.2

385.9
491.7973.50%
54.10%

317.0
317.5

431.3
586.8515

Future Work Reduce setup complexity– GUI– Improve mesh sampling to produce target points– Tighter integration of mesh sampling into application

 Visualization– Interpolate results to create heat map– Results by individual evaluation metric Non-linear optimization to maximize pose reachability

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Relevance Makes analysis of robotic systems more feasible (especially high-DOFsystems)
 Better analysis for single robot– Task/process oriented– Reach percentage– Visualize robot state at various target points

 Better analysis for multiple robot concepts– Compare reachability scores directly– Visualize reachable target “diffs” between various concepts

 Informs design decision more effectively than “gut feel”
17Questions?
Michael Ripperger
Southwest Research Institute6220 Culebra Rd.
San Antonio, TX, USA+1 (210) 522-6292
michael.ripperger@swri.org
www.swri.org
www.rosindustrial.org
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References
1. http://wiki.ros.org/reuleaux
2. http://demonstrations.wolfram.com/ManipulabilityEllipsoidOfARob
otArm
3. Wang, Xuhao & Zhang, Dawei & Zhao, Chen. (2017). The inverse
kinematics of a 7R 6-degree-of-freedom robot with non-spherical
wrist. Advances in Mechanical Engineering. 9. 168781401771498.
10.1177/1687814017714985.

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