Autonomous Robots

, Volume 1, Issue 1, pp 69–92 | Cite as

A bayesian approach to real-time obstacle avoidance for a mobile robot

  • Huosheng Hu
  • Michael Brady
Article

Abstract

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.

Keywords

mobile robots Bayesian approach obstacle avoidance sensor model 

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References

  1. Adams, M., Hu, H., and Probert, P.J. 1990. Towards a real-time architecture for obstacle avoidance and path planning in mobile robots. InProc. IEEE Int. Conf. Robotics and Automation, pp. 584–589, Cincinatti, USA.Google Scholar
  2. Arkin, R.C., 1989. Motor schema-based mobile robot navigation.Int. J. Robotics Research, 8(4):92–112.Google Scholar
  3. Berger, J.O. 1985.Statistical Decision Theory and Bayesian Analysis. Springer Verlag New York Inc., USA.Google Scholar
  4. Borenstein, J. and Koren, Y. 1989. Real-time obstacle avoidance for fast mobile robots.IEEE Trans. Systems Man and Cybernetics, 19(5):1179–1186.Google Scholar
  5. Borenstein, J. and Koren, Y. 1990. Real-time obstacle avoidance for fast mobile robots in cluttered environment. InProc. IEEE Int. Conf. Robotics and Automation, pp. 572–577.Google Scholar
  6. Borenstein, J. and Koren, Y. 1992. Noise rejection for ultrasonaic sensors in mobile robot applications. InProc. IEEE Int. Conf. Robotics and Automation, pp. 1727–1732, Nice, France, May.Google Scholar
  7. Brady, J.M., Cameron, S., Durrant-Whyte, H., Fleck, M., Forsyth, D., Noble, A., and Page, I., 1987. Progress towards a system that can acquire pallets and clean warehouses. InFourth Int. Symp. Robotics Research, Santa Cruz, August.Google Scholar
  8. Brady, J.M., Durrant-White, H., Hu, H., Leonard, J.J., Probert, P.J., and Rao, B.S.Y. 1990. Sensor-based control of AGVs.IEE Journal of Computing and Control Engineering, pp. 64–70, March.Google Scholar
  9. Brooks, R.A. 1986. A robust layered control system for a mobile robot.IEEE J. Robotics and Automation, 2:14.Google Scholar
  10. Cameron, A. 1989.A Bayesian Approach to Optimal Sensor Placement. PhD thesis, University of Oxford, U.K.Google Scholar
  11. Chatila, R. and Laumond, J.P. 1985. Position referencing and consistent world modeling for mobile robots. InProc. IEEE Int. Conf. Robotics and Automation, p. 138.Google Scholar
  12. Polaroid Corporation 1984.Ultrasonic Ranging System. Cambridge, Mass.Google Scholar
  13. Dickson, W. 1991.Image Structure and Model-based Vision. PhD thesis, University of Oxford.Google Scholar
  14. Durrant-Whyte, H.F. 1987.Integration, Coordination, and Control of Multi-Sensor Robot Systems. Kluwer Academic Press, Boston, MA.Google Scholar
  15. Elfes, A. 1987. Sonar-based real-world mapping and navigation. InProc. IEEE Int. Conf. Robotics and Automation, pp. 249–265.Google Scholar
  16. Elfes, A. 1990. Ocucupancy grids: A stochastic spatial representation for active robot perception. InProc. of the 6th Conference on Uncertainty in AI, July.Google Scholar
  17. Ferguson, T.S. 1967.Mathematical Statistics—A Decision Theoretic Approach. Academic Press, Inc.Google Scholar
  18. Hager, G. 1988.Active Reduction of Uncertainty in Multisensor Systems. Ph.D thesis, University of Pennsylvania.Google Scholar
  19. Hollier, R.H. 1987.Automated Guided Vehicles. IFS, London.Google Scholar
  20. Hu, H. 1992.Dynamic Planning and Real-time Control for a Mobile Robot. Ph.D thesis, University of Oxford.Google Scholar
  21. Hu, H., Brady, J.M., and Probert, P.J. 1993. Transputer architecture for sensor-guided control of mobile robots. InProc. of World Transputer Congress'93, Aachen, Germany, Sept.Google Scholar
  22. Hu, H. and Brady, M. 1992. Planning with uncertainty for a mobile robot. InProc. of 2nd Int. Conf. on Automation, Robotics and Computer Vision, Hyatt Regency, Singapore, 15–18 September.Google Scholar
  23. Khatib, O. 1986. Real-time obstacle avoidance for manipulators and mobile robots. InInt. J. Robotics Research, volume RR5:1, pp. 90–98.Google Scholar
  24. Koren, Y. and Borenstein, J. 1991. Potential field methods and their inherent limitations for mobile robot navigation. InProc. IEEE Int. Conf. Robotics and Automation, pp. 1398–1404, Sacramento, CaliforniaGoogle Scholar
  25. Kuc, R. and Viard, V.B. 1991. A physically based navigation strategy for sonar-guided vehicles.Int. J. Robotics Research, 10(2):75–87, April.Google Scholar
  26. Leonard, J.J. and Durrant-Whyte, H.F. 1992.Directed Sonar Sensing for Mobile Robot Navigation. Kluwer Academic Publishers.Google Scholar
  27. Moravec, H.P. and Elfes, A. 1985. High resolution maps from wide angle sonar. InProc. IEEE Int. Conf. Robotics and Automation, pp. 116–121.Google Scholar
  28. Premi, S. and Besant, C. 1983. A review of various vehicle guidance techniques that can be used by mobile robots or agvs. InProc. 2nd Int. Conf. on Automated Guided Vehicle Systems, pp. 195–209, Stuttgart, Germany.Google Scholar
  29. Rice, J.A. 1988.Mathematical Statistics and Data Analysis. Wadsworth and Brooks/Cole Advanced Books and Software, Pacific Grove, California.Google Scholar
  30. Stevens, P., Robins, M., and Roberts, M. 1983. Truck location using retroreflective strips and triangulation with laser equipment (turtle). InProc. 2nd European conf. on automated manufacturing, Birmingham, UK.Google Scholar
  31. Takeda, T., Kato, A., Suzuki, T., and Hosoi, M. 1986. Automated vehicle guidance using spotmark. InProc. IEEE Int. Conf. Robotics and Automation, p. 1346.Google Scholar
  32. Tsumura, T. 1986. Survey of automated guided vehicle in Japanese factory. InProc. IEEE Int. Conf. Robotics and Automation, p. 1329.Google Scholar
  33. Walter, S.A. 1987. The sonar ring: Obstacle detection for a mobile robot. InProc. IEEE Int. Conf. Robotics and Automation, pp. 1574–1579.Google Scholar
  34. Zhao, Y. and BeMent, S.L. 1990. A heuristic search approach for mobile robot trap recovery. InProc. SPIE, pp. 122–130.Google Scholar

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Huosheng Hu
    • 1
  • Michael Brady
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordU.K.

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