Scalable Control of Distributed Robotic Macrosensors

  • Brian Shucker
  • John K. Bennett


This paper describes a control mechanism by which large numbers of inexpensive robots can be deployed as a distributed remote sensing instrument, and in which the desired large-scale properties of the sensing instrument emerge from the simple pair-wise interactions of its component robots. Such sensing instruments are called distributed robotic macrosensors. Robots in the macrosensor interact with their immediate neighbors using a virtual spring mesh abstraction, which is governed by a simple physics model. By carefully defining the nature of the spring mesh and the associated physics model, it is possible to create a number of desirable global behaviors without any global control or configuration. Properties of the resulting macrosensor include arbitrary scalability, the ability to function in complex environments, sophisticated target tracking ability, and natural fault tolerance. We describe the control mechanisms that yield these results, and the simulation results that demonstrate their efficacy.


Sensor Network Mobile Robot Multiagent System Hexagonal Lattice Target Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Balch, T. and Hybinette, M. “Behavior-based coordination of large-scale robot formations.” Proceedings of the Fourth International Conference on Multiagent Systems (ICMAS’ 00). July 2000. pp. 363–364.Google Scholar
  2. 2.
    Baldassarre G. Nolfi S. Parisi D. “Evolving Mobile Robots Able to Display Collective Behaviors.” In Proceedings of the International Workshop on Self-Organisation and Evolution of Social Behaviour, pages 11–22, Monte Verità, Ascona, Switzerland, September 8–13, 2002.Google Scholar
  3. 3.
    Batalin, M. and Sukhatme, G.S. “Sensor Network-based Multi-Robot Task Allocation.” 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), October, 2003, Las Vegas, Nevada.Google Scholar
  4. 4.
    Brooks, R.A., “Integrated Systems Based on Behaviors”, SIGART Bulletin (2:4), August1991, pp. 46–50.Google Scholar
  5. 5.
    Delin, K “The Sensor Web: A Macro Instrument for Coordinated Sensing.” Sensors. Vol 2, 2002. pp. 270–285.Google Scholar
  6. 6.
    Deng, J. Han, R. Mishra, S. “A Performance Evaluation of Intrusion-Tolerant Routing in Wireless Sensor Networks,” IEEE 2nd International Workshop on Information Processing in Sensor Networks (IPSN’ 03), 2003, Palo Alto, California.Google Scholar
  7. 7.
    Gage, D. “Command Control for Many-Robot Systems.” Proceedings of AUVS-92. Huntsville, AL. June 1992.Google Scholar
  8. 8.
    Gage, D. “Randomized Search Strategies with Imperfect Sensors.” Proceedings of SPIE Mobile Robots VIII. Boston. September 1993. vol 2058, pp 270–279.Google Scholar
  9. 9.
    Giampapa, J. and Sycara, K. “Team-Oriented Agent Coordination in the RETSINA Multi-Agent System. Tech report CMU-RI-TR-02-34. Robotics Institute, Carnegie Mellon University. December 2002.Google Scholar
  10. 10.
    Gordon, D. Spears, W. Sokolsky, O. Lee, I. “Distributed Spatial Control, Global Monitoring and Steering of Mobile Physical Agents.” IEEE International Conference on Information, Intelligence, Systems. November 1999.Google Scholar
  11. 11.
    Gordon-Spears, D. and Spears, W. “Analysis of a Phase Transition in a Physics-Based Multiagent System.” In Proceedings of the FAABS’ 02 Workshop. 2002.Google Scholar
  12. 12.
    Hong, X. Gerla, M. Kwon, T. Estabrook, P. Pei, G. Bagrodia, R. “The Mars Sensor Network: Efficient, Energy Aware Communications” Proceedings of IEEE MILCOM. McLean, VA. 2001.Google Scholar
  13. 13.
    Howard, A. and Matarić, M. J. “Cover Me! A Self-Deployment Algorithm for Mobile Sensor Networks.” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2002), 2002.Google Scholar
  14. 14.
    Howard, A., Matarić, M.J., and Sukhatme, G.S. “Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem.” The 6 th International Symposium on Distributed Autonomous Robotic Systems, pp. 299–308.Google Scholar
  15. 15.
    Jung, B. and Sukhatme, G.S. “Tracking Targets using Multiple Robots: The Effect of Environment Occlusion”, Autonomous Robots, 13(3). 2002. pp. 191–205.MATHCrossRefGoogle Scholar
  16. 16.
    Koenig, S. and Liu, Y. “Terrain Coverage with Ant Robots: A Simulation Study.” In Proceedings of the International Conference on Autonomous Agents, pages 600–607, 2001.Google Scholar
  17. 17.
    Konolige, K. “A gradient method for realtime robot control.” In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2000.Google Scholar
  18. 18.
    López-Sánchez, M. Esteva, F. López de Màntaras, R. Sierra, C. Amat, J. “Map Generation by Cooperative Low-Cost Robots in Structured Unknown Environments.” Autonomous Robots, 5. pp. 53–61.Google Scholar
  19. 19.
    Nissanka, B. Chakraborty, A. Balakrishnan, H. “The Cricket Location-Support System.” 6 th ACM International Conference on Mobile Computing and Networking (MOBICOM). Boston, 2000.Google Scholar
  20. 20.
    Parker, L. “ALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots”, Proceedings of the 1994 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS’ 94)September 1994. pp 776–783.Google Scholar
  21. 21.
    Parker, L. “On the Design of Behavior-Based Multi-Robot Teams.” Advanced Robotics. 10(6) 1996. pp 547–578.CrossRefGoogle Scholar
  22. 22.
    Parker, L. and Emmons, B. “Cooperative Multi-Robot Observation of Multiple Moving Targets.” Proceedings of 1997 International Conference on Robotics and Automation, Volume 3, pp. 2082–2089.Google Scholar
  23. 23.
    Sahin_E. Franks N.R. “Measurement of Space: From Ants to Robots.” In Proceedings of WGW 2002: EPSRCIBBSRC International Workshop Biologically-Inspired Robotics: The Legacy of W. Grey Walter, pages 241–247, Bristol, UK, August 14–16, 2002.Google Scholar
  24. 24.
    Simmons, R. Apfelbaum, D. Burgard, W. Fox, D. Moors, M. Thrun, S. Younes, H. “Coordination for Multi-Robot Exploration and Mapping.” Seventeenth National Conference on Artificial Intelligence (AAAI). 2000.Google Scholar
  25. 25.
    Spears, W., and Gordon, D. “Using Artificial Physics to Control Agents.” In IEEE International Conference on Information, Intelligence, and Systems, 1999.Google Scholar
  26. 26.
    Thrun, S. Burgard, W. Fox, D. “A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping.” IEEE International Conference on Robotics and Automation. San Francisco. April 2000.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Brian Shucker
    • 1
  • John K. Bennett
    • 1
  1. 1.Department of Computer ScienceUniversity of ColoradoBoulder

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