Advertisement

RRS: Rapidly-Exploring Random Snakes a New Method for Mobile Robot Path Planning

  • K. Baizid
  • R. Chellali
  • R. Luza
  • B. Vitezslav
  • F. Arrichiello
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Recently, sampling-based path planning algorithms have been implemented in many practical robotics tasks. However, little improvements have been dedicated to the returned solution (quality) and sampling process. The aim of this paper is to introduce a new technique that improves the classical rapidly-exploring random trees (RRT) algorithm. First, the sampling step is modified in order to increase the number of possible solutions in the free space. Second, within the possible solutions, we apply an optimization scheme that gives the best solution in term of safety and shortness. The proposed solution, namely, rapidly-exploring random snakes (RRS) is a combination of a modified deformable Active Contours Model (called Snakes) and the RRT. The RRS takes the advantage of both RRT and deformable Snakes contours, respectively, in: rapidly searching new candidate nodes in the free space and circumnavigating obstacles by calculating a safe sub-path in the free space toward the new node created by the RRT. In comparison to the classical RRT, the proposed algorithm increases the probability of completeness, accelerates the convergence and generates a much safer and shorter open-loop solution, hence, increasing considerably the efficiency of the classical RRT. The proposed approach has been validated via numerical simulations and experimental results with a mobile robot.

Keywords

Path planning Active counter model Sampling algorithms Snake 

Notes

Acknowledgments

This research received funding from the European Community’s 7th Framework Programme under grant agreement n. 287617 (IP project ARCAS—Aerial Robotics Cooperative Assembly System).

References

  1. 1.
    LaValle, S. M., Planning algorithms. University of Illinois 1999–2004.Google Scholar
  2. 2.
    J.C. Latombe, “Robot Motion Planning”, Norwell, MA: Kluwer, 1991.Google Scholar
  3. 3.
    B. Chazelle. Approximation and decomposition of shapes. In J. T. Schwartz and C. K. Yap, editors, Algorithmic and Geometric Aspects of Robotics, pages 145–185. Lawrence Erlbaum Associates, Hillsdale, NJ, 1987.Google Scholar
  4. 4.
    Kuffner, J.J.; LaValle, S.M., “RRT-connect: An efficient approach to single-query path planning,” IEEE International Conference on Robotics and Automation, vol. 2, no., pp. 995–1001, 2000.Google Scholar
  5. 5.
    Ryad Chellali, Emmanuel Bernier, Khelifa Baizid, Mohamed Zaoui, “Interface for Multi-robots Based Video Coverage”, International Conference on Human-Computer Interaction, Vol. 6769, 2011, pp 203–210.Google Scholar
  6. 6.
    R. Pepy and M. Kieffer and E. Walter, "Reliably Safe Path Planning Using Interval Analysis", Progress in Industrial Mathematics at ECMI 2008, Mathematics in Industry 2010, pp 583–588.Google Scholar
  7. 7.
    Karaman, S., Frazzoli, E.: Sampling-based Algorithms for Optimal Motion Planning. IJRR 30(7), 846–894, 2011.Google Scholar
  8. 8.
    Bry, A.; Roy, N., “Rapidly-exploring Random Belief Trees for motion planning under uncertainty,” Robotics and Automation (ICRA), 2011 IEEE International Conference on, vol., no., pp. 723–730, 9–13 May 2011.Google Scholar
  9. 9.
    Garcia, I.; How, J.P., “Improving the Efficiency of Rapidly-exploring Random Trees Using a Potential Function Planner,” 44th IEEE Conference on Decision and Control and European Control Conference, vol., no., pp. 7965–7970, 12–15 Dec. 2005.Google Scholar
  10. 10.
    M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models”, Int. J. Computer Vision, vol. 1, pp. 321–331 1988.Google Scholar
  11. 11.
    Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” International Conference on Robotics and Automation. Proceedings., vol. 2, no., pp. 500–505, Mar 1985.Google Scholar
  12. 12.
    Warren, C.W., “Global path planning using artificial potential fields,” International Conference on Robotics and Automation, 1989. Proceedings., pp. 316,321 vol. 1, 14–19 May 1989.Google Scholar
  13. 13.
    Bhattacharya, P.; Gavrilova, M.L., “Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path,” Robotics & Automation Magazine, IEEE, vol. 15, no. 2, pp. 58–66, June 2008.Google Scholar
  14. 14.
    Mark de Berg, Otfried Cheong, Marc van Kreveld, and Mark Overmars. 2008. Computational Geometry: Algorithms and Applications, TELOS, Santa Clara, CA, USA.Google Scholar
  15. 15.
    Kavraki, L.E.; Svestka, P.; Latombe, J.-C.; Overmars, M.H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions onRobotics and Automation, vol. 12, no. 4, pp. 566–580, Aug 1996.Google Scholar
  16. 16.
    S. M. Lavalle and J. J. Kuffer, “Rapidly-exploring Random Trees: Progress and prospects”, Workshop on the Algorithmic Foundations of Robotics, 2000.Google Scholar
  17. 17.
    Burns, B.; Brock, O., “Single-Query Motion Planning with Utility-Guided Random Trees,” International Conference on Robotics and Automation, vol., no., pp. 3307–3312, 10–14 April 2007.Google Scholar
  18. 18.
    Akgun, B.; Stilman, M., “Sampling heuristics for optimal motion planning in high dimensions,” International Conference on Intelligent Robots and Systems (IROS), vol., no., pp. 2640–2645, 25–30 Sept. 2011.Google Scholar
  19. 19.
    Samuel Rodriguez, Xinyu Tang, Jyh-Ming Lien and Nancy M. Amato, “An Obstacle-Based Rapidly-Exploring Random Tree,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, vol. pp. 895–900\(,\) Orlando, Florida - May 2006.Google Scholar
  20. 20.
    Chenyang Xu; Prince, J.L., “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359–369, Mar 1998.Google Scholar
  21. 21.
    Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT press.Google Scholar
  22. 22.
    Khelifa Baizid, PhD thesis (2011) “Multi-robots Tele-operation Platform: Design and Experiments” Italian Institute of Technology & University of Genova, Italy.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • K. Baizid
    • 1
  • R. Chellali
    • 2
  • R. Luza
    • 3
  • B. Vitezslav
    • 3
  • F. Arrichiello
    • 1
  1. 1.University of Cassino and Southern LazioCassino (fr)Italy
  2. 2.Fondazione Instituto Italiano di Technologia (IIT)GenovaItaly
  3. 3.University of Technology Brno (BUT)CzechRepublic

Personalised recommendations