research-article

How search and its subtasks scale in N robots

Authors:

Prasanna Velagapudi,

Katia SycaraAuthors Info & Claims

HRI '09: Proceedings of the 4th ACM/IEEE international conference on Human robot interaction

Pages 141 - 148

https://doi.org/10.1145/1514095.1514122

Published: 09 March 2009 Publication History

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

[1]

B. Gerkey and M. Mataric. A formal framework for the study of task allocation in multi-robot systems. International Journal of Robotics Research, 23(9):939--954, 2004.

[2]

Miller, C. Modeling human workload limitations on multiple UAV control, Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting, 526--527, 2004.

[3]

Cummings, M. and Guerlain, S. An interactive decision support tool for real-time in-flight replanning of autonomous vehicles, AIAA Unmanned Unlimited Systems, Technologies, and Operations, 2004.

[4]

J. W. Crandall, M. A. Goodrich, D. R. Olsen, and C. W. Nielsen. Validating human-robot interaction schemes in multitasking environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 35(4):438--449, 2005.

Digital Library

[5]

J. Wang, M. and Lewis. Assessing coordination overhead in control of robot teams, Proceedings of 2007 IEEE International Conference on Systems, Man, and Cybernetics, 2645--2649, 2007.

[6]

J. Wang, H. Wang, and M. Lewis, M. Assessing Cooperation in Human Control of Heterogeneous Robots,Proceedings of the Third ACM/IEEE International Conference on Human-Robot Interaction (HRI'08), ACM, 2008.

Digital Library

[7]

Y. Cao, A. Fukunaga, and A. Kahng. Cooperative mobile robotics: Antecedents and directions, Autonomous Robots, 4, 1--23, 1997.

Digital Library

[8]

D.R Olsen and S.B. Wood, Fan-out: measuring human control of multiple robots, in Proceedings of the SIGCHI conference on Human factors in computing systems. 2004, ACM Press: Vienna, Austria. p. 231--238.

Digital Library

[9]

B. Trouvain, C. Schlick, and M. Mevert, Comparison of a map- vs. camera-based user interface in a multi-robot navigation task, in Proceedings of the 2003 International Conference on Robotics and Automation. 2003. p. 3224--3231.

[10]

B. Trouvain and H. Wolf. Evaluation of multi-robot control and monitoring performance. In Proceedings of the 2002 IEEE Int. Workshop on Robot and Human Interactive Communication, pages 111--116, September 2002.

[11]

C.M. Humphrey, C. Henk, G. Sewell, B. Williams, J. A. Adams. Evaluating a scaleable Multiple Robot Interface based on the USARSim Platform. 2006, Human-Machine Teaming Laboratory.

[12]

C. Wickens and J. Hollands. Engineering Psychology and Human Performance (3rd ed), NJ: Prentice-Hall Inc., 2000.

[13]

(UE 2) UnrealEngine2, http://udn.epicgames.com/Two/rsrc/Two/KarmaReference/KarmaUserGuide.pdf, accessed February 5, 2008.

[14]

S. Carpin, T. Stoyanov, Y. Nevatia, M. Lewis and J. Wang. Quantitative assessments of USARSim accuracy". Proceedings of PerMIS 2006

[15]

S. Carpin, J. Wang, M. Lewis, A. Birk and A. Jacoff. High fidelity tools for rescue robotics: Results and perspectives, Robocup 2005 Symposium, 2005.

[16]

Mathengine, MathEngine Karma User Guide, http://udn.epicgames.com/Two/KarmaReference/KarmaUserGuide.pdf, accessed May 3, 2005.

[17]

S. Carpin, M. Lewis, J. Wang, S. Balakirsky, C. Scrapper. (2006b). Bridging the gap between simulation and reality in urban search and rescue. Robocup 2006: Robot Soccer World Cup X, Springer, Lecture Notes in Artificial Intelligence, 2006.

Digital Library

[18]

M. Lewis, S. Hughes, J. Wang, M. Koes, and S. Carpin. Validating USARsim for use in HRI research, Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, Orlando, FL, 2005, 457--461

[19]

C. Pepper, S. Balakirsky, and C. Scrapper. Robot Simulation Physics Validation, Proceedings of PerMIS'07, 2007.

Digital Library

[20]

B. Taylor, S. Balakirsky, E. Messina and R. Quinn. Design and Validation of a Whegs Robot in USARSim, Proceedings of PerMIS'07, 2007.

Digital Library

[21]

M. Zaratti, M. Fratarcangeli and L. Iocchi. A 3D Simulator of Multiple Legged Robots based on USARSim. Robocup 2006: Robot Soccer World Cup X, Springer, LNAI, 2006.

Digital Library

[22]

S. Balakirsky, S. Carpin, A. Kleiner, M. Lewis, A. Visser, J. Wang and V. Zipara. Toward hetereogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup Rescue, Journal of Field Robotics, 2007

Digital Library

[23]

J. W. Crandall and M. L. Cummings. Developing Performance Metrics for the Supervisory Control of Multiple Robots. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 2007.

Digital Library

Cited By

Lee HCheon ELim CFischer K(2022)Configuring Humans: What Roles Humans Play in HRI Research2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI53351.2022.9889496(478-492)Online publication date: 7-Mar-2022
https://doi.org/10.1109/HRI53351.2022.9889496
Chen JBarnes M(2021)HUMAN–ROBOT INTERACTIONHANDBOOK OF HUMAN FACTORS AND ERGONOMICS10.1002/9781119636113.ch44(1121-1142)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119636113.ch44
Humann JPollard K(2019)Human Factors in the Scalability of Multirobot Operation: A Review and Simulation2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)10.1109/SMC.2019.8913876(700-707)Online publication date: Oct-2019
https://doi.org/10.1109/SMC.2019.8913876
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Index Terms

How search and its subtasks scale in N robots
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. External interfaces for robotics

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Published In

cover image ACM Conferences

HRI '09: Proceedings of the 4th ACM/IEEE international conference on Human robot interaction

March 2009

348 pages

ISBN:9781605584041

DOI:10.1145/1514095

General Chairs:
Matthias Scheutz
Indiana University, USA
,
François Michaud
Université de Sherbrooke, Canada
,
Program Chairs:
Pamela Hinds
Stanford University, USA
,
Brian Scassellati
Yale University, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 March 2009

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HRI09: International Conference on Human Robot Interaction

March 9 - 13, 2009

California, La Jolla, USA

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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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Cited By

Lee HCheon ELim CFischer K(2022)Configuring Humans: What Roles Humans Play in HRI Research2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI53351.2022.9889496(478-492)Online publication date: 7-Mar-2022
https://doi.org/10.1109/HRI53351.2022.9889496
Chen JBarnes M(2021)HUMAN–ROBOT INTERACTIONHANDBOOK OF HUMAN FACTORS AND ERGONOMICS10.1002/9781119636113.ch44(1121-1142)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119636113.ch44
Humann JPollard K(2019)Human Factors in the Scalability of Multirobot Operation: A Review and Simulation2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)10.1109/SMC.2019.8913876(700-707)Online publication date: Oct-2019
https://doi.org/10.1109/SMC.2019.8913876
Brewer RCerame EPursel EZimmermann ASchaefer K(2018)Manned-Unmanned Teaming: US Army Robotic Wingman VehiclesAdvances in Human Factors in Simulation and Modeling10.1007/978-3-319-94223-0_9(89-100)Online publication date: 27-Jun-2018
https://doi.org/10.1007/978-3-319-94223-0_9
Rossi AStaffa MRossi S(2017)Supervisory Control of Multiple Robots Through Group CommunicationIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2016.26065629:1(56-67)Online publication date: Mar-2017
https://doi.org/10.1109/TCDS.2016.2606562
Wang Y(2016)Regret-Based Automated Decision-Making Aids for Domain Search Tasks Using Human-Agent Collaborative TeamsIEEE Transactions on Control Systems Technology10.1109/TCST.2015.250896324:5(1680-1695)Online publication date: Sep-2016
https://doi.org/10.1109/TCST.2015.2508963
Wright JChen JBarnes MHancock P(2016)The Effect of Agent Reasoning Transparency on Automation Bias: An Analysis of Response PerformanceVirtual, Augmented and Mixed Reality10.1007/978-3-319-39907-2_45(465-477)Online publication date: 19-Jun-2016
https://doi.org/10.1007/978-3-319-39907-2_45
Rosenfeld AAgmon NMaksimov OAzaria AKraus S(2015)Intelligent agent supporting human-multi-robot team collaborationProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832513(1902-1908)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832415.2832513
Wang Y(2015)Regret-based Bayesian sequential decision-making for human-agent collaborative search tasks2015 American Control Conference (ACC)10.1109/ACC.2015.7172238(5738-5743)Online publication date: Jul-2015
https://doi.org/10.1109/ACC.2015.7172238
Kosti SKaminka GSarne DBazzan AHuhns MLomuscio AScerri P(2014)Towards effective user-guided robot search (extended abstract)Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems10.5555/2615731.2617500(1415-1416)Online publication date: 5-May-2014
https://dl.acm.org/doi/10.5555/2615731.2617500
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