How search and its subtasks scale in N robots

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How search and its subtasks scale in N robots

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ACM/IEEE International Conference on Human-Robot Interaction archive
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction table of contents

La Jolla, California, USA

SESSION: Situation awareness, interface design and usability table of contents

Pages 141-148

Year of Publication: 2009

ISBN:978-1-60558-404-1

Authors

Huadong Wang	University of Pittsburgh, Pittsburgh, PA, USA
Michael Lewis	University of Pittsburgh, Pittsburgh, PA, USA
Prasanna Velagapudi	Carnegie Mellon University, Pittsburgh, PA, USA
Paul Scerri	Carnegie Mellon University, Pittsburgh, PA, USA
Katia Sycara	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

The present study investigates the effect of the number of controlled robots on performance of an urban search and rescue (USAR) task using a realistic simulation. Participants controlled either 4, 8, or 12 robots. In the fulltask control condition participants both dictated the robots' paths and controlled their cameras to search for victims. In the exploration condition, participants directed the team of robots in order to explore as wide an area as possible. In the perceptual search condition, participants searched for victims by controlling cameras mounted on robots following predetermined paths selected to match characteristics of paths generated under the other two conditions. By decomposing the search and rescue task into exploration and perceptual search subtasks the experiment allows the determination of their scaling characteristics in order to provide a basis for tentative task allocations among humans and automation for controlling larger robot teams. In the fulltask control condition task performance increased in going from four to eight controlled robots but deteriorated in moving from eight to twelve. Workload increased monotonically with number of robots. Performance per robot decreased with increases in team size. Results are consistent with earlier studies suggesting a limit of between 8-12 robots for direct human control.

REFERENCES

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