Soldier-Robot Teaming: Effects of Multimodal 

Collaboration on Team Communication for Robot 

Reconnaissance 

by Shaun Hutchins, Keryl Cosenzo, Michael Barnes, Theodric Feng, and 

Krishna Pillalamarri 

ARL-TR-5385 October 2010 

Approved for public release; distribution unlimited. 
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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5425 

ARL-TR-5385 October 2010 

Soldier-Robot Teaming: Effects of Multimodal 

Collaboration on Team Communication for Robot 

Reconnaissance 

Shaun Hutchins 

Alion Science and Technology Inc. 

Keryl Cosenzo, Michael Barnes, Theodric Feng, and Krishna Pillalamarri

Human Research and Engineering Directorate, ARL

Approved for public release; distribution unlimited. 
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Soldier-Robot Teaming: Effects of Multimodal Collaboration on Team 

Communication for Robot Reconnaissance 

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Shaun Hutchins, Keryl Cosenzo, Michael Barnes, Theodric Feng, and Krishna 

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14. ABSTRACT 

The impacts on performance of three different forms of communication (radio, chat, and tactile belt) were explored in the 

context of a small unmanned ground vehicle (SUGV) target identification task. The target identification task required a 

commander with knowledge of target locations and access to a digital map displaying the current SUGV position and 

orientation to direct a Soldier remotely operating the SUGV to the targets using a finite set of 11 commands. The study 

revealed no evidence of a loss of Soldier performance using the tactile belt communications channel. The finding suggests that

the tactile use of haptic signals may be feasible, a potentially important finding for situations requiring covert communications. 

15. SUBJECT TERMS

multimodal displays, robotics, field experiment 

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Shaun Hutchins 

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iii

Contents 

List of Figures v

List of Tables v

1. Introduction 1

1.1 General Background ........................................................................................................1

1.2 Soldier Robot Teaming Objective ...................................................................................2

1.3 Research Questions .........................................................................................................2

1.3.1 Time to Target .....................................................................................................3

1.3.2 Number of Communications ...............................................................................3

2. Method 3

2.1 Participants ......................................................................................................................3

2.2 Apparatus.........................................................................................................................3

2.2.1 TALON Robots ...................................................................................................3

2.2.2 AMREL Dismounted Controller .........................................................................4

2.2.3 Tactor Belt ...........................................................................................................5

2.2.4 Targets .................................................................................................................5

2.2.5 Questionnaires .....................................................................................................5

2.3 Experiment Design and Methodology .............................................................................6

2.3.1 Experimental Design ...........................................................................................6

2.3.2 Experimental Dismount Team .............................................................................6

2.3.3 Communication Type ..........................................................................................7

2.3.4 Dependent Variables ...........................................................................................8

2.3.5 Procedure .............................................................................................................8

2.4 Data Collection ................................................................................................................9

2.4.1 Data Processing ...................................................................................................9

3. Analyses and Results 10

3.1 Time to Identify Targets ................................................................................................10

3.1.1 Data Screening and Selection ............................................................................10

3.1.2 Analysis .............................................................................................................10

3.1.3 Results ...............................................................................................................10
iv

3.2 Number of Navigational Communications ....................................................................11

3.2.1 Data Screening and Selection ............................................................................11

3.2.2 Analyses ............................................................................................................11

3.2.3 Results ...............................................................................................................11

3.3 Exploratory Analysis .....................................................................................................12

3.3.1 Data Screening and Selection ............................................................................12

3.3.2 Analyses ............................................................................................................13

3.3.3 Results ...............................................................................................................13

3.4 NASA TLX and SART Analyses ..................................................................................13

3.4.1 Data Screening and Selection ............................................................................13

3.4.2 Analyses ............................................................................................................13

3.4.3 Results ...............................................................................................................14

4. Conclusion 14

5. References 16

List of Symbols, Abbreviations, and Acronyms 18

Distribution List 19
v

List of Figures 

Figure 1. TALON robot. .................................................................................................................4

Figure 2. AMREL dismounted controller. ......................................................................................4

Figure 3. Tactor belt. .......................................................................................................................5

List of Tables 

Table 1. Randomly assigned exposure orders of communication conditions and course 

layouts. .......................................................................................................................................6

Table 2. Post-hoc tests on time to identify (seconds) by target type. ...........................................11

Table 3. Communication count pos- hoc tests on target type. ......................................................12

Table 4. Communication frequencies. ..........................................................................................12

Table 5. Time to identify targets: median split. ...........................................................................13

Table 6. Communication frequency median split. ........................................................................13

 
vi

INTENTIONALLY LEFT BLANK. 
1

1. Introduction 

In 2004, the U.S. Army Tank-Automotive Research Development and Engineering Center 

(TARDEC), in partnership with the U.S. Army Research Laboratory (ARL) Human Research 

and Engineering Directorate (HRED), pursued a 5-year program U.S. Army Technology 

Objective (ATO). The purpose of the ATO was to develop the tools, techniques, and autonomy 

to maximize mounted and dismounted control of ground and air unmanned systems and optimize 

Soldier-robot and robot-robot ground and air teams. Development included a scalable user 

interface for robotic control. The interface maximizes multi-function Soldier performance for 

primary tasks while minimizing unique training requirements, achieved by optimizing and 

standardizing the required interactions and managing the workload associated with the control of 

unmanned ground and air systems. This report highlights the Robotics Collaboration ATO 

Capstone Experiment on small robot control. 

1.1 General Background 

As the emerging technologies of the Army’s Future Combat System (FCS) are introduced to the 

battlefield, Soldiers will increasingly face new challenges in workload management. A shifting 

force structure will bring increasing responsibilities for the next generation Soldier, who will be 

tasked with effectively using and protecting robotic assets in addition to performing other 

primary missions. TARDEC and HRED are addressing issues in human-robot interaction (HRI) 

under the Robotics Collaboration (RC) ATO program. The key program goal is to understand 

HRI issues in order to develop technologies and mitigations that enhance HRI performance in 

future combat environments. 

Robots have been and continue to be effective in combat tasks that are “dull, dirty, and 

dangerous” (Carafano and Gudgel, 2007). Remotely controlled robotic unmanned air vehicles 

(UAVs) and unmanned ground vehicles (UGVs) play a vital role in current operations, including 

disarming roadside bombs and performing ground- and air-based patrol missions. However, 

these systems involve the cognitive resources of the operators, who are continuously engaged in 

teleoperating the systems and manipulating the sensors or payloads during the mission. Future 

military operations require the Soldier to be as unencumbered by equipment and weapon 

constraints as possible. At the same time, the number of possible systems that are available at 

even the squad level will increase dramatically from the current force. In particular, the use of 

robotic systems will enhance the operator’s safety, flexibility, and lethality during urban 

missions―but at a price. Performance is enhanced by the ability to remotely “see” and “shoot” 

in buildings, disarm explosive devices, and view around corners. The price is the additional 

workload and loss of situation awareness (SA) when the Soldier is most vulnerable. Robotic 

teleoperation focuses the operator on the robotic task, to the detriment of personal security (Chen 

and Joyner, 2009). 
2

The twin goals of the Soldier-robot teaming research were to understand HRIs in a military 

environment and assess technologies that mitigate undesirable effects. One area of interest was 

the use of innovative solutions such as multimodal interfaces. In general, ARL researchers found 

that using tactile and auditory cueing both separately and as redundant cues improved Soldier 

multitasking performance (Chen, Haas, and Barnes, 2007; Chen and Terrence, 2008; Haas, 

2007). Tactile cueing was found to be more flexible because it did not require the Soldier to 

disambiguate between front and back signals for navigation tasks. Perhaps more importantly, 

tactile cueing uses communications channels that are currently underused in military 

environments (Barnes, in press). For example, our research verified that tactile information 

could be used for covert communications using a limited lexicon that was easily learned by 

Soldier participants (Gilson, Redden, and Elliott, 2007). Ease of learning tactile cues was a 

reoccurring trend in many of the published studies, further suggesting the advantages of tactile 

communications for Soldier robot teaming operations (Jones and Sarter, 2008). 

For example, Pettit, Redden, and Carstens (2006) translated Army hand and arm signals to a 

covert tactile communication system, resulting both in improved navigation aids and as a silent 

signaling environment for army snipers (Gilson et al., 2007). However, the potential uses of 

covert, low-bandwidth tactile modes go beyond communication networks among Soldiers. As 

robots develop more environmental awareness, tactile communications might be possible as a 

two-way conduit between robots and Soldier teams (Barnes, in press). The current study was 

designed to explore this possibility in a realistic field environment at Ft. Bliss in El Paso, TX, 

with experienced Soldiers. Soldiers conducted search missions in desert environment, not too 

unlike the Iraqi desert. The Soldier operators were given minimal training using a TALON robot 

with three communication modes: chat (visual text), radio, and tactile. The Soldier operator was 

directed by a remotely located commander to target locations hidden in the desert terrain. The 

operator in turn directed the TALON robot to various locations using a specially designed 

interface based on the commander’s inputs in one of the three communications modes. This 

emulated Soldier teams or Soldier robot teams working in close concert and sharing SA. 

1.2 Soldier Robot Teaming Objective 

The objective of the current research was to explore potential relationships between modes of 

communication, i.e., auditory, visual, or tactile, and task performance for shared control of 

dismounted operations with a small unmanned ground vehicle (SUGV). Specifically, the 

relationships were explored in a SUGV teleoperation task in which critical navigation and map 

information was the focus of the dismount team’s shared control. 

1.3 Research Questions 

This research sought to address the impact of the type of communication, i.e., auditory, visual, or 

tactile, on a dismounted team’s performance on a target identification task. To help answer the 

overall question, several specific questions were addressed in the experiment.
3

1.3.1 Time to Target 

Are there differences between the three levels of communications type (radio, chat, and tactile) 

with regard to the average time to identify a target? Are there differences between the three 

levels of target type (large, medium, and small) with regard to the average time to identify a 

target? Is there an interaction between communications type and target type with regard to the 

average time to identify a target? 

1.3.2 Number of Communications 

Are there differences between the three levels of communications type (radio, chat, and tactile) 

with regard to the average number of communications required to identify a target? Are there 

differences between the three levels of target type (large, medium, and small) with regard to the 

average number of communications required to identify a target? Is there an interaction between 

communications type and target type with regard to the average number of communications 

required to identify a target? 

We hypothesized that the tactile communications would result in improved latency and accuracy 

performance compared to traditional communication modes (i.e., radio). 

2. Method 

2.1 Participants 

Volunteer participants consisted of 13 active-duty Soldiers from the 2nd Combined Arms 

Battalion 5th Brigade (Army Experiment Task Force) located at Ft. Bliss, TX. All of the 

Soldiers were male ranging in age from 20–31 with an average of 24.08 years of age. 

2.2 Apparatus 

2.2.1 TALON Robots 

The SUGV for this experiment was the TALON robot. The TALON is a man-portable (45 kg) 

rugged robot that can negotiate through all types of terrain and can withstand all types of weather 

(figure 1). 
4

Figure 1. TALON robot. 

2.2.2 AMREL Dismounted Controller 

The controller of the SUGV allows the dismounted Soldier to operate the SUGV and share 

information with his team member about mission-critical events. It is a tablet-based controller 

(figure 2) with a resistive touch screen and a stylus that is ruggedized for the extreme weather 

conditions and suited with military connection capability. The controller has the following 

dimensions: 

• 9.8 in wide ×7.4 in diameter × 1.65 in high 

• 4.2 lb with primary battery 

• 4.7 lb with second battery 

Figure 2. AMREL dismounted controller.
5

2.2.3 Tactor Belt 

The tactor belt was worn around the waist and contained eight individual tactors (the C-2 model, 

Engineering Acoustics, Inc.; figure 3). The group of tactors functioned together and vibrated in a 

unique pattern to distinguish discrete commands. 

Figure 3. Tactor belt.

2.2.4 Targets 

Large, medium, and small targets were used in each experimental mission. A mission was the 

experimental course. The large target was a construction barrel ~4 ft high. The medium target 

was an orange cone ~18 in high. The small target was a white pipe ~12 in long. 

2.2.5 Questionnaires 

Subjective workload and SA ratings were measured with the National Aeronautics and Space 

Administration-Task Load Index (NASA-TLX) (Hart and Staveland, 1987) and the Cognitive 

Compatibility Situation Awareness Technique Questionnaire (CC-SART) (Taylor, 1990), 

respectively. The NASA-TLX is a multi-dimensional rating procedure that derives an overall 

workload score based on a weighted average of ratings on six subscales (Mental Demand, 

Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration). The CCSART is a subjective rating of SA, in which participants rate their experience with the task on 

three dimensions (Activation of Knowledge, Ease of Reasoning, and Level of Processing). Level 

of Processing is the degree to which the situation involves, at the low level, natural automatic, 

intuitive, and associated processing or, at the high level, analytic, considered, conceptual, and 

abstract processing. Ease of Reasoning is the degree to which the situation, at a low level, is 

confusing and contradictory or, at a high level, is straightforward and understandable. Activation 

of Knowledge is the degree to which the situation, at the low level, is strange and unusual or, at 

the high level, is recognizable and familiar. Workload and SA were assessed at the end of each 

mission. 
6

2.3 Experiment Design and Methodology 

2.3.1 Experimental Design 

The experiment was a 3 × 3 within-subject design. Communication type and target type were 

two independent variables of experimental interest. Communication type had three categories: 

radio (audio), chat (visual), and tactile. Target type had three levels (large, medium, and small). 

Participants completed two missions in each of the three communication conditions; therefore, 

each participant completed six missions. A mission consisted of the participant driving the robot 

through a course with embedded targets (one large target, one medium target, and two small 

targets) that he had to identify. 

There were three courses used in the experiment. Due to logistical constraints, the three courses 

were set up prior to the experiment and target locations were fixed for the duration of the 

experiment. The size of the course areas, the terrain features, and the distances between targets 

within each course were approximately equivalent between the courses. On each course there 

were four targets randomly dispersed in the course area. So in one course the first target could 

be a small target and in another course the first target could be a large target. Participants ran the 

robot through each course in succession, course 1, course 2, and then course 3. This sequence 

was repeated for a total of six missions. Table 1 shows the randomly assigned order of exposure 

to the various communication conditions and course layouts. 

Table 1. Randomly assigned exposure orders of communication conditions and course layouts. 

Participants 

Mission 1 Mission 2 Mission 3 Mission 4 Mission 5 Mission 6 

Course 1 Course 2 Course 3 Course 1 Course 2 Course 3 

1, 12, 13 Radio Radio Chat Chat Tactor Tactor 

2, 11 Tactor Tactor Chat Chat Radio Radio 

3, 10 Radio Radio Tactor Tactor Chat Chat 

4, 9 Tactor Tactor Radio Radio Chat Chat 

5, 8 Chat Chat Radio Radio Tactor Tactor 

6, 7 Chat Chat Tactor Tactor Radio Radio 

The three types of communication conditions produced a total of six possible exposure orders. 

Repetitions of the six potential exposure orders to the three communications conditions was 

balanced to the best extent possible across 13 participants such that two of the exposure orders 

were assigned two repetitions and one was allowed 3 repetitions. The assignment of participant 

to exposure condition was random without replacement. Radio, then chat, and then tactors was 

the exposure order with three randomly assigned participants. 

2.3.2 Experimental Dismount Team 

The experimental team consisted of a commander, a robotic controller, and a robotic follower. 

The role of the commander was filled by an experimental team member. The commander was in 

charge of relaying navigational commands to the robotic controller, directing him towards the 
7

targets. The role of the robotic controller was filled by a Soldier participant. The robotic 

controller was in control of the TALON mobility and responsible for identified targets. The role 

of the robotic follower was filled by an experimental team member. The robotic follower was 

responsible for the safety of the TALON while on the course. 

2.3.3 Communication Type 

Three communication modalities were used in the study: Chat interface, tactor belt, and radio. 

The chat, or visual, modality provided the participant (i.e., robotic controller) and the 

commander with a suite of communications on the AMREL tablet. A discrete set of 

communications were represented with interface buttons, one button per communication. 

Selecting one of the buttons in the suite sent the representative chat message to the team member 

and was displayed as a text message on the AMREL tablet. The tactor belt contained eight 

individual tactors. The tactors functioned together and vibrated in a unique pattern for each of 

the discrete communications. As in the chat condition, selecting one the buttons in the suite of 

discrete communications on the AMREL tablet sent the representative message to the team 

member; however, in this condition, the team member’s belt would vibrate according to the type 

of message sent. The radio condition, or auditory modality, used hand-held two-way radios. All 

communications were shared using the radios, but the types of communications were restricted to 

the same set of discrete communications that were available in the chat and tactor conditions. 

The commander and the robotic operator sent and received communications via the AMREL 

displays by using either tactile belt (wore by commander and operator) or chat messaging. 

Communication between the commander and the participant in the dismount team was two way 

within the communication modality of the condition. The commander in the dismount team had 

a digital map on his AMREL display showing the current SUGV position (from GPS) and 

orientation as well as target locations. The commander directed the participant with navigational 

commands. The participant teleoperated the TALON to the critical areas based on the 

commander’s direction; however, the commander did not “micromanage” the operator (i.e., 

specify which way to go around bushes). Using the condition-appropriate communication 

modality, the commander indicated when the robot was in the target vicinity and the participant 

would scan to find the target. Commands were limited to a discrete set of 11 communications: 

• Scan forward 

• Scan left 

• Scan right 

• Transit 180° 

• Transit forward 

• Transit left 
8

• Transit right 

• Found 

• Halt 

• Rally 

• Move out 

2.3.4 Dependent Variables 

Two primary dependent measures were collected: time to identify targets and the total number of 

navigational communications. 

• Time to identify targets was defined as the number of seconds from when the 

communication to “move out” was given at the beginning of a course segment until the 

“found” communication was given upon identifying a target. 

• Number of navigational communications was defined as the frequency of navigation 

communications issued from when the communication to “move out” was given at the 

beginning of a course segment until the “found” communication was given upon 

identifying a target. 

2.3.5 Procedure 

The participant was verbally briefed on his role in the teleoperation tasks in the experiment. The 

brief included a description of the modes of communication and target types central to the 

experimental tasks, as well as a task demonstration. The Soldier then practiced the experimental 

tasks with each communication modality on a separate training course with targets. Practice was 

structured such that a communication modality was first demonstrated to the Soldier, and then 

the Soldier completed a training run within that communication modality, repeating this practice 

structure across all three communication conditions. Radio was always introduced first, then 

chat, and lastly tactor. The exception to this practice procedure was for the tactor 

communication modality. The Soldier experienced every communication condition multiple 

times, and then recognition of communications was tested until 100% accuracy for identifying 

communications was achieved. This recognition test was repeated later, just prior to beginning 

the tactor experiment trials for further practice and competence. 

The Soldier then began the sequence of experimental missions. Each participant completed one 

mission in a single modality, and then repeated the mission in the same modality a second time. 

The two repetitions of each of the three modalities yielded a total of six missions per participant. 

Immediately following the first pair of sequential missions within a single communications 

modality, the Soldier was administered the NASA-TLX and SART questionnaires. The Soldier 

performed the second pair of missions followed with associated questionnaires; and lastly the 

third pair of missions with associated questionnaires. Within a mission, the procedure was to 
9

first get verbal confirmation from the Soldier of readiness to begin. The commander then issued 

a “move out” command to begin the mission, followed by a series of additional navigational 

commands directing the Soldier to each target within the mission. The participant reported 

“Target found” upon identifying each target and the commander would respond with a 

“Confirm.” After receiving the confirmation, the Soldier progressed to the next target in the 

mission. Upon identifying the fourth target in the mission, the commander would issue a “Rally” 

command signifying the end of the experimental trial and indicating that the Soldier was to 

teleoperate the TALON back to the mission start point. 

2.4 Data Collection 

2.4.1 Data Processing 

The data for each participant were captured in the AMREL event data logs and by an 

experimental observer; however, the event data logs were incomplete, including complete sets of 

only chat and tactor data. Radio communications via the two-way radio was independent of the 

data logging mechanism. The event data logs recorded with millisecond precision represented a 

potentially more accurate source of target times than the records of the experimental observer, 

but the records kept by the experimental observer were complete for all three conditions, even 

though no doubt subject to rounding bias due to the hand recorded event records. In order to 

ensure accurate target time data, in the conditions where the redundant data existed, the time to 

each target within each communication condition was examined for both the data logs and the 

observer records. The examination revealed near equivalent records with slightly shorter 

observer record times than data log times. Since the observer records were complete, consistent 

across all conditions, and nearly equivalent to the data logs in redundant conditions, the observer 

records were selected for the time analysis. Otherwise, the three modes would be compared 

using two different recording mechanisms: the AMREL for the chat and tactor data and the 

manual recording for the audio data, leaving open the possibility of a confound between type of 

recording and communication mode. In the observer records, there were 16 total instances of 

either missing or ambiguous mission data. Each event was isolated and triangulated with data 

log elapsed times, observer record elapsed times, and observer trial notes. Each of the 16 

missing or ambiguous target times was corrected using time estimates across the three sources. 

To ensure the communication frequency data was accurate, the number of communications per 

target within each communication condition was examined in both the data logs and the observer 

records, again in the conditions where the redundant data existed. The examination of 

communication frequencies revealed that records between the two sources closely matched in the 

chat and tactor conditions, but varied considerably in the radio condition. The only means of 

communicating in the chat and tactor conditions was via the tablet, and therefore, transmitted 

with the tablet, and as a result, captured in the logs. Consequently, the data logs had to be 100% 

accurate for these two conditions and were preserved for analysis. This was not the case for the 

radio condition. Further examination of the observer trial notes and observer records revealed 
10

that repeated radio commands were recorded in the observer records, but only a single instance 

of the corresponding command was recorded in the data logs. It was inferred that in instances 

when the verbal radio command had to be repeated several times by the commander, the 

commander only entered the command in the tablet once; perhaps due to the multitasking 

demand of using the radios and tablet simultaneously. This provided evidence that the data logs 

were likely less accurate in radio command frequency than the observer records and that the 

observer records for radio command frequency were a valid, reliable, and unbiased substitution 

for analysis. 

3. Analyses and Results 

3.1 Time to Identify Targets 

3.1.1 Data Screening and Selection 

The time data for each participant was first plotted by communications type and target type, and 

then visually inspected. It was observed that the majority of the outlying or extreme values 

existed for trials in excess of 150 s. A review of the observer run sheets indicated that trials 

approximating or exceeding 150 s often involved errors of some kind. Example errors were 

communication failures, failure of AMREL dismounted controller software, TALON 

malfunctions, or simply participant confusion. The data did not meet normality assumptions, 

showing evidence of skew, kurtosis, and significant non-normality according to the ShapiroWilk test of normality. A log transformation reduced skew to two instances and kurtosis to three 

instances, and only one condition failed the test of normality; however, filtering data points in 

excess of 150 s reduced skew to 0 instances and kurtosis to two instances, and resulted in 

normally distributed data in all conditions. Due to better meeting the normality assumptions 

with the filtered data, analysis was carried out with this latter data set. 

3.1.2 Analysis 

A 3 × 3 repeated measures analysis of variance (ANOVA) was performed on the time-toidentify-targets data based on communication type and target type. 

3.1.3 Results 

The interaction of communications type x target type was not significant, F(4,48)=1.01, p<.41, 

ηp

2

=0.08. Communications type was not significant, F(2,24)=1.3, p<.29, ηp

2

=0.1. Target type 

was significant, suggesting an effect of the size of targets on RT, F(2,24)=9.72, p<.001, 

ηp

2

=0.45. The mean time to identify for the large target was M=66.99s, for the small target was 

M=80.34 s, and the medium target was M=87.17s. Post-hoc tests using the Bonferroni 

adjustment for multiple comparisons revealed that Soldiers were significantly faster identifying 
11

the large targets versus the small or medium targets, but that the results for the small target did 

not differ from the medium target. The significant post-hoc results are shown in table 2. 

Table 2. Post-hoc tests on time to identify (seconds) by target type. 

 Mean Diff SE Sig d' 

Large target Medium target 13.35 3.71 0.01 0.93 

Large target Small target 20.18 5.62 0.01 1.07 

3.2 Number of Navigational Communications 

3.2.1 Data Screening and Selection 

The number of navigational communications for each participant in each condition were plotted 

by target type and visually inspected. Visually nothing appeared out of normal range, but the 

data within conditions did show evidence of minor skew, kurtosis, and small departures from 

normality using the Shapiro-Wilk test of normality. A natural log transformation was performed 

on the data to eliminate these problems. The transformation corrected any normality violations. 

Log linear is similar to a chi square in that it can handle interactions, which makes it a 

nonparametric equivalent to the 3 × 3 parametric ANOVA concurrently performed on the data. 

The two complimentary analyses provide a more comprehensive evaluation of the data so that 

we could make a better judgment about patterns in the data. Additionally, since the 

communication data were frequency counts, they were also compiled as total conditional 

frequencies for a tabular analysis. The total number of communications was counted across all 

participants within each condition. Due to two small targets in each mission, the large and 

medium targets each had two repetitions across conditions, but the small target segment had four 

repetitions. To account for the extra repetitions for the small target, the total frequency was 

calculated and then divided by two to make the small target frequency equivalent relative to the 

large and medium targets frequencies. 

3.2.2 Analyses 

First, a 3 × 3 repeated measures ANOVA was performed on the navigational communications 

data with communication type and target type. Next, a log linear analysis was performed on the 

cross tabulated navigational communication frequency data. 

3.2.3 Results 

The interaction of communications type x target type was not significant, F(4,48)=1.23, p<.31, 

ηp

2

=0.09. Communications type was not significant, F(2,24)=1.89, p<.17, ηp

2

=0.14. Target type 

was significant, suggesting an effect of the size of targets on the number of communications, 

F(2,24)=10.99, p<.001, ηp

2

=.48. Using raw frequencies, the mean count for the large target was 

M=5.88, for the small target was M=7.48, and for the medium target was M=7.73. Post-hoc tests 

using the Bonferroni adjustment for multiple comparisons revealed that Soldiers did require 
12

significantly fewer communications to identify the large target versus both the small and medium 

targets, but the small target results did not differ from the medium target results. The significant 

post-hoc results are shown in table 3 using the log transformed data. 

Table 3. Communication count pos- hoc tests on target type. 

 Mean Diff SE Sig d' 

Large target Medium target 0.30 0.07 0.002 1.35 

Large target Small target 0.27 0.06 0.003 0.93 

The log linear analysis supported the results of the previous ANOVA. Starting with the saturated 

model, the final model kicked out the interaction term leaving only the first order effects. Of the 

two first order effects, target type clearly had the stronger impact on the model with a significant 

log likelihood chi square value nearly 3× as large as the likelihood value for communications 

type. The final model with both first order effects was not significant (using a strict alpha 

criterion of .05) χ

2

(4,1648)=9.08, p<.059. However, the models with either target type or 

communications type alone were significant. The log likelihood chi square value 

χ

2

(2,1648)=8.96, p<.011 for communications type reveals that fewer communications were 

observed in the tactor condition relative to the chat and radio conditions than were expected. 

The observed and expected frequencies are shown in table 4. Likewise, χ

2

 (2,1648)=22.09, 

p<0.001 for target type indicates that fewer communications were observed for the large target 

condition relative to the small and medium conditions than were expected. 

Table 4. Communication frequencies. 

Observed N Expected N Residual

Tactor 498 549.3 –51.3 

Chat 597 549.3 47.7 

Radio 553 549.3 3.7 

Total 1648 — — 

3.3 Exploratory Analysis 

3.3.1 Data Screening and Selection 

The subjective threshold that was noted regarding the 150-s ceiling for errors raised some 

questions about the data in hindsight. Research on human error (Horrey and Wickens, 2007) 

suggests that often the most important details regarding error can be in the tails of the 

distribution, and that, studies focusing on the centrality of the distributions may mask important 

evidence. Consequently, the upper and lower halves of the time-to-identify-targets and 

communication-frequency data sets were contrasted for differences. The median of each data set 

was computed and the data recoded as either above or below the median. 
13

3.3.2 Analyses 

Chi square analyses were run on the median split data for time and communication frequency. 

3.3.3 Results 

The results of the chi square on the median split time data did not show evidence of a 

proportional difference in time to identify targets across communication types. The pattern of 

instances shown in table 5 reflects a greater proportion of trials below the median than above it 

in the tactor condition relative to the chat and radio conditions. This pattern was not inferentially 

supported though given χ

2

(2, 117)=0.27, p<0.87, Cramer’s V=0.05. 

Table 5. Time to identify targets: median split. 

 Tactor Chat Radio Total 

Below median 21 19 19 59 

Above median 18 20 20 58 

Total 39 39 39 117 

The results of the median split on communication frequency did show evidence of a proportional 

difference in frequency of communications across communication type. The pattern of instances 

shown in table 6 reflects a greater proportion of trials below the median than above it in the 

tactor condition relative to the chat and radio conditions. The χ

2 test did not show statistical 

significance at the conventional α= 0.05 level but this pattern did show evidence of inferential 

support given χ

2

(2, 117)=5.41, p<0.067, Cramer’s V=0.22. 

Table 6. Communication frequency median split. 

Tactor Chat Radio Total 

Below median 26 16 19 61 

Above median 13 23 20 56 

Total 39 39 39 117 

3.4 NASA TLX and SART Analyses 

3.4.1 Data Screening and Selection 

Both the NASA TLX and SART data sets were screened for illegal values. None were found. 

The rating data revealed non-normality using the Shapiro-Wilk test of normality and 

consequently treated ordinally in the analysis to resolve the lack of normality. 

3.4.2 Analyses 

Both data sets were analyzed using the Friedman nonparametric test for dependent samples. 

Only the primary variable of interest, communication type, was used in this analysis. 
14

3.4.3 Results 

None of the six dimensions of workload showed any evidence of differences between 

communication types. Likewise, none of the three dimensions of SA indicated any differences 

between communication types. 

4. Conclusion 

There was considerable noise in the data caused by intermittent problems with the TALON, 

among other uncontrollable factors. The power analysis allowed us to conclude that additional 

participants would not have changed the results. In particular, we found that communication 

mode has no real effect on target identification time, i.e., no mode performed significantly better 

or worse than the others. This in itself is an important finding because it suggests that the 

tactical use of haptic signals for covert communications may be feasible in light of finding no 

evidence to support differential performance within the haptic form of communication. Thus, 

haptics could potentially be a desirable tactical option. While not implying performance is 

equivalent among communication modes, we obtained no evidence that would lead us to believe 

the converse, i.e., there was a performance decrement for tactile communications. This is 

gratifying because of the difficulty of the task, which consisted of finding targets in a previously 

unknown environment using an unfamiliar tactile lexicon while navigating a TALON robot with 

which they had limited familiarity. 

Gilson et al. (2007) suggested that robotic operators will be able to use low bandwidth tactile 

signals to communicate covertly with their chain of command and eventually communicate with 

future robots with enhanced cognitive abilities. Perhaps of equal importance is that tactile 

communications permit Soldiers to use a virtually unused communications channel to increase 

SA without impinging on their visual or auditory resources (Barnes, in press). Also, there was 

evidence that the tactile cueing might actually be an efficient means of communication, as 

reflected in the results that indicted a reduced number of communications were necessary for 

target location in this mode; however, this finding requires additional research for verification. 

Also, it is important to note that the 13 Soldiers were relatively efficient in using the TALON to 

find targets in desert terrain. It would be a mistake to assume this was an easy task. The targets 

were hidden in the desert foliage so that the targets were never in plain sight of the robot sensor 

system. Also, during this time of year, there was a great deal of vegetation on the desert floor 

including complex sand dunes overgrown with thorny creosote bushes because of the monsoon 

rains. The training was minimal; total time was >1 h. The training took place in a separate area 

to ensure that Soldiers were not familiar with target locations. Nevertheless, the Soldiers were 

able to navigate the TALON to most of the targets rapidly. There was a consistent target effect. 

Logically, it would seem the small target would be the most difficult target to find and 
15

consequently reflect a great search time and/or communication frequency. This was not the case. 

Rather, the results showed evidence that medium targets were the most challenging to identify. 

In summary, the results reinforced evidence from previous research that tactile communications 

are a tactically useful form of communication for Soldier-robot interactions. 

Past research has shown that the tactile channel is beneficial when used redundantly with either 

the auditory or visual channel. In these paired instances, a tactile cue may increase both speed 

and accuracy of Soldier responses (e.g., Hempel and Altinsoy, 2005). The current research 

suggests that in environmental manipulations that make normal communications modalities 

ineffective, the tactile channel may serve as a tactically effective alternative. The findings in this 

experiment suggest that the inclusion of the tactile mode for communication poses no detriment 

in performance, and perhaps even an enhancement. These results make a good argument to 

examine transitioning this communications channel to a variety of operations. Subsequent 

experimentation should include examining chat, radio, and tactile communication modes in day 

versus night or safe versus covert environments, and possibly further exploring tactile 

communications as a redundant channel within the various environments. 
16

5. References 

Barnes, M. J. Soldier-Robot Teaming in Future Military Systems: An Overview. In HumanRobot Interactions in Future Military Operations, Barnes, M. J.; Jentsch, F. D., eds.; 

Ashgate Publishing: Hampshire, UK, in press. 

Carafano, J. J.; Gudgel, A. The Pentagon’s Robots: Arming the Future. The Heritage 

Foundation: Backgrounder 2007, 2096, 1–6. 

Chen, J.Y.C.; Haas, E. C.; Barnes, M. J. Human Performance Issues and User Interface Design 

for Teleoperated Robots. IEEE Transactions on Systems, Man, and Cybernetics--Part C: 

Applications and Reviews 2007, 37 (6), 1231–1245. 

Chen, J.Y.C.; Joyner, C. T. Concurrent Performance of Gunner’ and Robotic Operator’s Tasks 

in a Multi-Tasking Environment. Military Psychology 2009, 21 (1), 98–113. 

Chen, J.Y.C.; Terrence, P. I. Effects of Tactile Cueing on Concurrent Performance of Military 

and Robotics Tasks in a Simulated Multi-Tasking Environment. Ergonomics 2008, 51 (8), 

1137–1152. 

Gilson , R. D.; Redden, E. S.; Elliott, L. R. Remote Tactile Displays for Future Soldier; ARLSR-0152; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, 2006. 

Haas, E. C. The Role of Emerging Multimodal Technologies in Enhancing Combat and Civilian 

System Safety. Journal of the American Society of Safety Engineer 2007. 

Hart, S. G.; Staveland, L. E. Development of NASA-TLX (Task Load Index): Results of 

Empirical and Theoretical Research. In Human Mental Workload, Hancock, A.; Meshkati, 

H., eds., Elsevier: Amsterdam, 1987. 

Hempel, T.; Altinsoy, E. Multimedia/Multimodal Displays. In Handbook of Human Factors in 

Web Design, Proctor, R.W.; Vu, K. P., eds., Lawrence Erlbaum: London, 2005, pp 134–145. 

Horrey, W.; Wickens, C. In-Vehicle Glance Duration. Distributions, Tails, and Model of Crash 

Risk. Journal of the Transportation Research Board, Transportation Research Board of the 

National Academies, Washington, D.C. 2007, 2018, pp 22–28. 

Jones, L. A.; Sarter, N. B. Tactile Displays: Guidance for Their Design and Application. 

Human Factors 2008, 50 (1), 90–111. 

Pettit, R. A.; Redden, E. S.; Carstens, C. C. Comparison of Hand and Arm Signals to a Covert 

Tactile Communication System in a Dynamic Environment; ARL-TR-3838; U.S. Army 

Research Laboratory: Aberdeen Proving Ground, MD, 2006. 
17

Taylor, R. M. Situational Awareness Rating Technique (SART): The Development of a Tool 

For Aircraft Systems Design. In Situational Awareness in Aerospace Operations. Neuilly 

Sur Seine, France: NATO-AGARD, 1990. AGARD-CP-478. 

 
18

List of Symbols, Abbreviations, and Acronyms 

ANOVA Analysis of Variance 

ARL U.S. Army Research Laboratory 

CC-SART Compatibility Situation Awareness Technique Questionnaire 

ATO U.S. Army Technology Objective 

FCS Future Combat System 

HRI human robot interaction 

HRED Human Research and Engineering Directorate 

NASA-TLX National Aeronautics and Space Administration-Task Load Index 

RC Robotics Collaboration 

SA situation awareness 

SUGV small unmanned ground vehicle 

TARDEC U.S. Army Tank-Automotive Research Development and Engineering Center 

UAVs unmanned air vehicles 

UGVs unmanned ground vehicles 
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