View Planning via C-space Entropy for Efficient Exploration with Eye-in-Hand Systems

  • Yong Yu
  • Kamal K. Gupta
Conference paper
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 271)


We present an implemented sensor-based planner for motion planning and exploration for eye-in-hand systems. A model-based motion planner is used to plan paths within the known part of the environment to further sense the unknown part of the environment. Each sensing action is viewed as gaining information about the status of configuration space. We introduce the notion of C-space entropy as a measure of ignorance or lack of information of C-space. The next view is planned so as to maximize expected entropy reduction (MER), or equivalently, expected information increase. Experimental results demonstrate that MER criterion results in efficient exploration of unknown environments and that the planner can make a robot arm move around safely (without collisions) while carrying out exploratory and purposive tasks in unknown environments.


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  1. [1]
    V.J. Lumelsky and E Cheung. Real-time collision avoidance in teleoperated whole-sensitive robot arm manipulators. IEEE Transactions on Systems, Man and Cybernetics, 23(1):194–203, Jan.–Feb 1993.CrossRefGoogle Scholar
  2. [2]
    K. Hirai, M. Hirose, M. Haikawa, and T. Takenaka. The development of honda humanoid robot. In Proceedings of IEEE International Conference on Robotics and Automation, pages 1321–1326, 1998.Google Scholar
  3. [3]
    Kamal Gupta and Yong Yu. On eye-sensor based path planning for robots with non-trivial geometry/kinematics. accepted for IEEE International Conference on Robotics and Automation, 2001.Google Scholar
  4. [4]
    Kamal K. Gupta and Angel del Pobil, editors. Practical Motion Planning in Robotics: Current Approaches and Future Directions. John Wiley, 1998.Google Scholar
  5. [5]
    L. Kavraki, P. Svestka, J. Latombe, and M. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4):556–580, Aug 1996.CrossRefGoogle Scholar
  6. [6]
    J. Ahuactzin and A. Portilla. A basic algorithm and data structure for sensor-based path planning in unknown environment. In Proceedings of IEEE International Conference on Intelligent Robots and Systems, 2000.Google Scholar
  7. [7]
    Y. Yu and K. Gupta. Sensor-based roadmaps probabilistic roadmaps: Experiments with an eye-in-hand system. Advanced Robotics, 14(8), August 2000. A version also appeared In Proceedings of IEEE/RSI International Conference on Intelligent Robot and System, pages 1707–1714, 1999.Google Scholar
  8. [8]
    Y. Yu and K. Gupta. Sensor-based motion planning for manipulator arms: An eye-in-hand system. In IEEE International Conference on Robotics and Automation video session, 2000.Google Scholar
  9. [9]
    Y. Yu and K. Gupta. An information theoretic approach to view point planning for motion planning of eye-in-hand systems. In Proceedings of 31st International Symposium on Robotics, pages 306–311, 2000.Google Scholar
  10. [10]
    Yong Yu and Kamal Gupta. An information theoretical approach to view planning with kinematic and geometric constraints. accepted for IEEE International Conference on Robotics and Automation, 2001.Google Scholar
  11. [11]
    Yong Yu. An information theoretical incremental approach to sensor-based motion planning for eye-in-hand systems. Ph.D. Thesis. School of Engineering Science, Simon Eraser University. Canada. 2000.Google Scholar
  12. [12]
    E. Kruse, R. Gutsche, and F. Wahl. Effective, iterative, sensor based 3-d map building using rating functions in configuration space. In Proceedings of IEEE International Conference on Robotics and Automation, pages 1067–1072, 1996.Google Scholar
  13. [13]
    P. Renton, M. Greenspan, H. Elmaraghy, and H. Zghal. Plan-n-scan: A robotic system for collision free autonomous exploration and workspace mapping. Journal of Intelligent and Robotic System, 24:207–234, 1999.CrossRefGoogle Scholar
  14. [14]
    S. Hutchinson and A. Kak. Planning sensing strategies in a robot work cell with multi-sensor capabilities. IEEE Transaction on Robotics and Automation, 5(6):765–783, December 1989.CrossRefGoogle Scholar
  15. [15]
    D. Stoyan and W.S. Kendall. Stochastic geometry and its applications. J. Wiley, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Yong Yu
    • 1
  • Kamal K. Gupta
    • 1
  1. 1.School of Engineering ScienceSimon Fraser UniversityBurnabyCanada

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