Advertisement

A Framework for Unknown Environment Manipulator Motion Planning via Model Based Realtime Rehearsal

  • Dugan Um
  • Dongseok Ryu
  • Sungchul Kang
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)

Abstract

In this paper, we propose a novel framework for an unknown environment path planning of manipulator type robots. Unknown environment motion planning, by its nature, requires a sensor based planning approach. The problem domain of unknown environment planning is notoriously hard, especially for difficult cases. The framework we propose herein is a sensor based planner composed of a sequence of multiple MBPs (Model Based Planners) in the notion of cognitive planning using realtime rehearsal. That is, by the proposed framework, one can use a combination of model based planners as tactical tools to resolve location specific problems in overall planning endeavor. The enabling technology for the realtime rehearsal is a sensitive skin type sensor introduced in the paper. We describe the developed sensor and demonstrate the feasibility of solving a difficult unknown environment problem using the introduced sensor based planning framework up to 3 DOF linked manipulator.

Keywords

sensor based planning randomized sampling unknown environment motion planning collision avoidance cognitive planning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, J.Y., Choset, H.: Sensor-based Planning for a Rod-shaped Robot in Three Dimensions: Piecewise Retracts of R^3 x S^2. International Journal of Robotics Research 24(5), 343–383 (2005)CrossRefGoogle Scholar
  2. 2.
    Mehrandezh, M., Gupta, K.K.: Simultaneous path planning and free space exploration with skin sensor. In: Proc. of the IEEE Int. Conf. on Robotics & Automation, Washington DC, pp. 3838–3843 (May 2002)Google Scholar
  3. 3.
    Yu, Y., Gupta, K.: Sensor-based probabilistic roadmaps: Experiments with and eye-in-hand system. Journal of Advanced Robotics, 515–536 (2000)Google Scholar
  4. 4.
    Cheung, E., Lumelsky, V.: A sensitive skin system for motion control of robot arm manipulators. Jour. of Robotics and Autonomous Systems 10, 9–32 (1992)CrossRefGoogle Scholar
  5. 5.
    Kavraki, L., Latombe, J.-C.: Randomized preprocessing of configuration space for fast path planning. In: Proc. Int. Conf. on Robotics & Automation, San Diego, pp. 2138–2145 (May 1994)Google Scholar
  6. 6.
    Um, D.: Sensor Based Randomized Lattice Diffusion Planner for Unknown Environment Manipulation. In: Proc. Of the IEEE Int. Conf. on Intelligent Robots and Systems, Beijing, China, pp. 5382–5387 (2006)Google Scholar
  7. 7.
    LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: Progress and prospects. In: Proc. of Workshop on the Algorithmic Foundations of Robotics (2000)Google Scholar
  8. 8.
    Kurniawati, H., Hsu, D.: Workspace importance sampling for probabilistic roadmap planning. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, vol. 2, pp. 1618–1623 (September 2004)Google Scholar
  9. 9.
    Zheng, S., Hsu, D., Reif, J.H.: Narrow passage sampling for probabilistic roadmap planning. IEEE Transactions on Robotics 21, 1105–1115 (2005)CrossRefGoogle Scholar
  10. 10.
    Lumelsky, V.: Algorithmic and complexity issues of robot motion in an uncertain environment. Journal of Complexity, 146–182 (1987)Google Scholar
  11. 11.
    Amato, N.M., Wu, Y.: A randomized roadmap method for path and manipulation planning. In: IEEE Int. Conf. on Robotics & Automation, pp. 113–120 (1996)Google Scholar
  12. 12.
    Torabi, L., Gupta, K.: Integrated View and Path Planning for an Autonomous six-DOF Eye-in-hand Object Modeling System. In: IEEE Int. Conf. on Intelligent Robots and Systems, Taiwan (October 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Mechanical EngineeringTexas A&M University-CCCorpus ChristiUSA

Personalised recommendations