Potential functions based sampling heuristic for optimal path planning
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Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.
KeywordsMotion planning Convergence rate Optimal path planning Artificial potential fields Sampling based algorithms
- Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. arXiv preprint arXiv:1005.0416.
- Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.Google Scholar
- Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In IEEE international conference on robotics and automation (pp. 1398–1404).Google Scholar
- Kuffner Jr, J., & Latombe, J.-C. (2000). Interactive manipulation planning for animated characters. In IEEE international conference on computer graphics and applications (pp. 417–418).Google Scholar
- Lamiraux, F., & Laumond, J.-P. (1996). On the expected complexity of random path planning. In IEEE international conference on robotics and automation (pp. 3014–3019).Google Scholar
- LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Report No. TR 98-11, Computer Science Department, Iowa State University.Google Scholar
- Lee, M. C. & Park, M. G. (2003). Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In IEEE/ASME international conference on advanced intelligent mechatronics (pp. 735–740).Google Scholar
- Lin, M., Manocha, D., Cohen, J., & Gottschalk, S. (1996). Collision detection: Algorithms and applications. In Algorithms for Robotic Motion and Manipulation (WAFR96) (pp. 129–141).Google Scholar
- Matsumoto, K., Ishikawa, M., Inaba, M., & Shimoyama, I. (2012). Assistive robotic technologies for an aging society. In IEEE special issue on quality of life technology (pp. 2429–2441).Google Scholar
- Perez, A., Karaman, S., Shkolnik, A., Frazzoli, E., Teller, S., & Walter, M. R. (2011). Asymptotically optimal path planning for manipulation using incremental sampling-based algorithms. In IEEE/RSJ international conference on intelligent robots and systems (pp. 4307–4313).Google Scholar
- Qureshi, A. H., Iqbal, K. F., Qamar, S. M., Islam, F., Ayaz, Y., & Muhammad, N. (2013a). Potential guided directional-rrt* for accelerated motion planning in cluttered environments. In IEEE international conference mechatronics and automation (pp. 519–524).Google Scholar
- Qureshi, A. H., Mumtaz, S., Iqbal, K. F., Ali, B., Ayaz, Y., Ahmed, F., Muhammad, M. S., Hasan, O., Kim, W. Y., & Ra, M. (2013b). Adaptive potential guided directional-rrt. In IEEE international conference on robotics and biomimetics (pp. 1887–1892).Google Scholar