Autonomous Robots

, Volume 20, Issue 2, pp 125–136

Issues in the scaling of multi-robot systems for general problem solving

Article

Abstract

Problem solving using multi-agent robotic systems has received significant attention in recent research. Complex strategies are required to organize and control these systems. Biological-inspired methodologies are often employed to bypass this complexity, e.g. self-organization. However, another line of research is to understand the relationship between low-level behaviors and complex high-level strategies. In this paper, we focus on understanding the interference caused in multi-robotic systems for the problem of search and tagging. Given a set of targets that must be found and tagged by a set of robots, what are the effects of scaling the number of robots and sensor ranges? Intuitively, increasing robot numbers, or sensor strength would seem beneficial. However, experience suggests that path and sensor interference caused by increased robots, increased targets, and sensor range will be harmful. The following investigation uses several abstract models to elucidate the issues of robot scaling and sensor noise.

Keywords

Multiple robots systems Scaling Sensor issues 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gustafson, D. Rapaka, V. and DeLoach, S. 2004. A comparison of algorithms for teams of robots. In Proceedings of SMC 2004, The Hague.Google Scholar
  2. Mataric, M. 1995. Issues and approaches in the design of collective autonomous agents. Robotics and Autonomous Systems, 16:321–331.Google Scholar
  3. Behring, C. Bracho, M. Castro, M. and Moreno, J. A. 2000. An algorithm for robot path planning with cellular automata. In Proceedings of the Fourth International Conference on Cellular Automata for Research and Industry, pp. 11–19. Springer-Verlag.Google Scholar
  4. Aylett, R. S. and Barnes, D. P. 1998 A multi-robot architecture for planetary rovers. In Proceedings of the 5th ESA Workshop on Advanced Space Technologies for Robotics and Automation.Google Scholar
  5. Malrey Lee. 2003. Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm. Information Sciences, 155:43–60.Google Scholar
  6. Burgard, W, Moors, M. and Schneider, F. 2002. Collaborative exploration of unknown environments with teams of mobile robots. In Proceedings of the Dagstuhl Seminar on Plan-based Control of Robotic Agents. Springer Verlag.Google Scholar
  7. Hsiang, T.-R., Arkin, E. M., Bender, M., Fekete, S. P. and Mitchell, J. S. B. 2002. Algorithms for rapidly dispersing robot swarms in unknown environments. In 5th International Workshop on Algorithmic Foundations of Robotics.Google Scholar
  8. Stachniss, C. and Burgard, W. 2003. Mapping and exploration with mobile robots using coverage maps. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  9. Rosencrantz, M. Gordon, G. and Thrun, S. 2003. Locating moving entities in indoor environments with teams of mobile robots. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, Melbourne, Australia.Google Scholar
  10. Parker, L. E. 2002. Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous Robots, 12(3):231–255Google Scholar
  11. Fox, D. Burgard, W. Kruppa, H. and Thrun, S. 2000. A probabilistic approach to collaborative multi-robot localization. Autonomous Robots, 8(3):325–344.Google Scholar
  12. Balch, T. and Arkin, R. C. 1995. Communication in reactive multiagent robotic systems. Autonomous Robots, 1(1):27–52.Google Scholar
  13. Mataric, M. 1992. Behavior-based control: Main properties and implications. In Proceedings of IEEE International Conference on Robotics and Automation, Workshop on Intelligent Control Systems, pp. 46–54, Nice, France.Google Scholar
  14. Balch, T. and Arkin, R.C. 1998. Behavior-based formation control for multirobot teams. IEEE Transactions on Robotics and Automation, 14:926–939.Google Scholar
  15. Scerri, P., Xu, Y., Liao, E., Lai, G. and Sycara. K. 2004. Scaling teamwork to very large teams. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, New York.Google Scholar
  16. Gage, D. W. 1993. Randomized search strategies with imperfect sensors. In Mobile Robots VIII, volume 2058 of SPIE, pp. 270–279.Google Scholar
  17. Gustafson, S. and Gustafson, D. 2004. Scaling issues with robot search and tagging. In S. Colombano, editor, RoboSphere 2004 Workshop, NASA Ames Research Center, USA.Google Scholar
  18. Brogan D. C. and Hodgins, J. K. 1997. Group behaviors for systems with significant dynamics. Autonomous Robots, 4:137–153.Google Scholar
  19. Farinelli, A. Scerri, P. and Tambe, M. 2003. Building largescale robot systems: Distributed role assignment in dynamic, uncertain domains. In Proceedings of Workshop on Representations and Approaches for Time-Critical Decentralized Resource, Role and Task Allocation, Melbourne, Australia.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.GE Global ResearchUSA
  2. 2.Kansas State UniversityUSA

Personalised recommendations