RoboLeader for reconnaissance by a team of robotic vehicles

Snyder, Mark; Qu, Zhihua; Chen, Jessie Y. C.; Barnes, Michael

doi:10.1109/cts.2010.5478467

Cited by 11 publications

(10 citation statements)

References 26 publications

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“…Typical tasks include reconnaissance and surveillance. RoboLeader has the capability of collecting information from subordinate robots with limited autonomy (e.g., collision avoidance and self-guidance to reach target locations), making tactical decisions and coordinating the robots by issuing commands, waypoints, or motion trajectories [7] [20]. …”

Section: Simulatormentioning

confidence: 99%

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Chen

Barnes

Kenny

2011

Proceedings of the 6th International Conference on Human-Robot Interaction

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A military multitasking environment was simulated to examine the effects of unreliable automation on the performance of robotics operators. The main task was to manage a team of four ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes based upon developments in the mission environment. RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers. Results showed that the type of RoboLeader unreliability (falsealarm vs. miss prone) affected operator's performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density for multiple performance measures. Participants with higher spatial ability performed better on the two tasks that required the most visual scanning (target detection and route editing). Participants' attentional control impacted their overall multitasking performance, especially during their execution of the secondary tasks (communication and gauge monitoring).

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Section: Simulatormentioning

confidence: 99%

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Chen

Barnes

Kenny

2011

Proceedings of the 6th International Conference on Human-Robot Interaction

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“…The operator can, therefore, better focus on other tasks requiring his/her attention. More specifically, RoboLeader can collect information from subordinate robots with limited autonomy (e.g., collision avoidance and self-guidance to reach target locations), making tactical decisions and coordinating the robots by issuing commands, waypoints, or motion trajectories (Snyder et al, 2010). In typical mission situations, RoboLeader would recommend route revisions when encountering environmental events that require robots to be rerouted.…”

Section: Roboleadermentioning

confidence: 99%

Human-Agent Teaming for Multi-Robot Control: A Literature Review

Jessie¹,

Barnes²

2013

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The literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots; appropriate human trust in the automated systems; maintenance of human operator's situation awareness; individual differences in human-agent (H-A) interaction; and retention of human decision authority. A number of approaches, from flexible automation to autonomous agents, were reviewed and their advantages and disadvantages were discussed. Also discussed were two key human performance issues (trust and situation awareness) related to H-A teaming for multi-robot control and some promising user interface design solutions to address these issues. Some key individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.

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“…The Operator Control Unit (OCU) of the MIX Testbed (Figure 1) was modeled after the Tactical Control Unit developed under the ARL Robotics Collaborative Technology Alliance. RoboLeader has the capability of collecting information from subordinate robots with limited autonomy (e.g., collision avoidance and self-guidance to reach target locations), making tactical decisions, and coordinating the robots by issuing commands, waypoints, or motion trajectories (Snyder et al, 2010). See the Procedure section for more details about the user interface of RoboLeader.…”

Section: Methodsmentioning

confidence: 99%

“…To achieve a better balance of enhancing autonomy and capability while simplifying human-robot interaction, a robotic surrogate for the human operator, RoboLeader, was developed under the U.S. Army Research Laboratory's (ARL) Director's Research Initiative Program (Snyder, Qu, Chen, & Barnes, 2010). RoboLeader is an intelligent agent that interprets an operator's intent and issues detailed command signals to a team of robots of lower capabilities.…”

Section: Current Studymentioning

confidence: 99%

Supervisory Control of Multiple Robots

2012

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Objective: A military multitasking environment was simulated to examine the effects of an intelligent agent, RoboLeader, on the performance of robotics operators.Background: The participants' task was to manage a team of ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes on the basis of battlefield developments.Method: In the first experiment, RoboLeader was perfectly reliable; in the second experiment, RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers.Results: RoboLeader, when perfectly reliable, was helpful in reducing the overall mission times. The type of RoboLeader imperfection (false-alarm vs. miss prone) affected operators' performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density (clutter of the visual scene) for multiple performance measures. Participants' attentional control and video gaming experience affected their overall multitasking performance. In both experiments, participants with greater spatial ability consistently outperformed their low-spatial-ability counterparts in tasks that required effective visual scanning.Conclusion: Intelligent agents, such as RoboLeader, can benefit the overall human-robot teaming performance. However, the effects of type of agent unreliability, tasking requirements, and individual differences have complex effects on human-agent interaction.Application: The current results will facilitate the implementation of robots in military settings and will provide useful data to designs of systems for multirobot control.

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RoboLeader for reconnaissance by a team of robotic vehicles

Cited by 11 publications

References 26 publications

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Effects of unreliable automation and individual differences on supervisory control of multiple ground robots

Human-Agent Teaming for Multi-Robot Control: A Literature Review

Supervisory Control of Multiple Robots

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