International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 7, No. 5, 8 Apr. 2015
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Mobile Robot Path Planning by RRT* in Dynamic Environments
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Author(s)
Index Terms
Mobile Robot, Navigation, Path planning, Sampling-based Methods, RRT*
Abstract
Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.
Cite This Paper
Roudabe Seif, Mohammadreza Asghari Oskoei, "Mobile Robot Path Planning by RRT* in Dynamic Environments", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.5, pp.24-30, 2015. DOI:10.5815/ijisa.2015.05.04
Reference
[1]1-S.Lavalle. Planning Algorithms. Cambridge University Press, 2006.
[2]J. Barraquand and J.C. Latombe. Motion planning: A distributed representation approach. The International Journal of Robotics Research, 10(6), 1991.
[3]L. Kavraki, P. Svestka, J. Latombe, and M. Overmars. Probabilistic roadmaps for fast path planning in high dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12, 1996.
[4]L. E. Kavraki. Random Networks in Configuration Space for Fast Path Planning. PhD thesis, 1995.
[5]M. H. Overmars and P. ˇ Svestka. A probabilistic learning approach to motion planning. In WAFR: Proceedings of the workshop on Algorithmic foundations of robotics, 1995.
[6]H. Choset, K.M. Lynch, S. Hutchinson, G. Kantor,W. Burgard, L. Kavraki, and S. Thrun. Principles of Robot Motion: Theory, Algorithms, and Implementations. The MIT Press, 2005.
[7]D. Hsu, J. C. Latombe, and H. Kurniawati. On the probabilistic foundations of probabilistic roadmap planning. Int. J. Rob. Res., 25(7), 2006.
[8]G. Song and N. M. Amato. Randomized motion planning for car-like robots with C-PRM. In Proc. of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 37 – 42, 2001.
[9]M. S. Branicky, S. M. Lavalle, K. Olson, and L. Yang. Quasi randomized path planning. In Proc. of the IEEE International Conference on Robotics and Automation, pages 1481–1487, 2001.
[10]M. Pivtoraiko, R. A. Knepper, and A. Kelly. Differentially constrained mobile robot motion planning in state lattices. J. Field Robot. 26(3):308–333, 2009. ISSN 1556-4959. doi:http://dx.doi.org/10.1002/rob.v26:3.
[11]J. Kuffner and S. M. Lavalle. Randomized kinodynamic planning. In Proc. of the IEEE International Conference on Robotics and Automation, pages 473–479, 1999.
[12]S. M. Lavalle and J. Kuffner. RRT-connect: An efficient approach to single-query path planning. In Proc. of the IEEE International Conference on Robotics and Automation, pages 995–1001, 2000.
[13]D. Hsu, R. Kindel, J.-C. Latombe, and S. Rock. Randomized kinodynamic motion planning with moving obstacles. In Algorithmic and Computational Robotics: New Directions, pages 247–264, 2001.
[14]S.R. Lindemann and S.M. LaValle. Current issues in sampling-based motion planning. In P. Dario and R. Chatila, editors, Eleventh International Symposium on Robotics Research, pages 36–54. Springer, 2005.
[15]L. E. Kavraki, M. N. Kolountzakis, and J. Latombe. Analysis of probabilistic roadmaps for path planning. IEEE Transactions on Robotics and Automation, 14(1):166–171, 1998.
[16]D. Hsu, J. Latombe, and H. Kurniawati. On the probabilistic foundations of probabilistic roadmap planning. International Journal of Robotics Research, 25:7, 2006.
[17]N. Lv and Z. Feng, "numerical potential field and ant colony optimization based path planning in dynamic environment", the sixth IEEE world congress on intelligent control and automation , vol 2,pages 8966-8970, Oct 2006.
[18]D. Hsu, R. Kindel, J. Latombe, and S. Rock. Randomized kinodynamic motion planning with moving obstacles. International Journal of Robotics Research, 21(3):233–255, 2002.
[19]E. Frazzoli, M. Dahleh, and E. Feron. Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1):116–129, 2002.
[20]A. Bhatia and E. Frazzoli. Incremental search methods for reachability analysis of continuous and hybrid systems. In R. Alur and G.J. Pappas, editors, Hybrid Systems: Computation and Control, number 2993 in Lecture Notes in Computer Science, pages 142–156. Springer-Verlag, Philadelphia, PA, March 2004.
[21]J. Cortes, L. Jailet, and T. Simeon. Molecular disassembly with RRT like algorithms. In IEEE International Conference on Robotics and Automation (ICRA), 2007.
[22]M. Zucker, J. Kuffner, and M. Branicky. Multiple RRTs for rapid replanning in dynamic environments. In IEEE Conference on Robotics and Automation, 2007.
[23]S. M. LaValle and J. J. Kuffner. Randomized kinodynamic planning. International Journal of Robotics Research, 20(5):378–400, May 2001.
[24]M. S. Branicky, M. M. Curtis, J. A. Levine, and S. B. Morgan. RRTs for nonlinear, discrete, and hybrid planning and control. In IEEE Conference on Decision and Control, 2003.
[25]D. Ferguson and A. Stentz. Anytime RRTs. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2006.
[26]Bessiere P., J.M.Ahuactzin.EI-Ghazali Talbi and E.Mazer,"Ariadne's clew algorithm: Global planning with local methods", IEEE Int.Conf.on Intelligent Robots and Systems, 1993.
[27]A. Stents, "The Focussed D* Algorithm for Real-Time Replanning", proceedings of the International Joint Conference on Artificial Intelligence, pages 1652-1659, Aug 1995.
[28]Sanchez A.G.and J.Latombe,"Using a PRM planner to compare cantralized and decoupled planning for multi-robot systems",IEEE Int.Conf.on Robotics and Automation,2112-2119, 2002.
[29]Karaman, Sertac, and Emilio Frazzoli. "Sampling-based algorithms for optimal motion planning." The International Journal of Robotics Research 30.7, 846-894, 2011.