Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot
Motion planning problem though widely studied in robotics is a difficult problem. It finds a feasible path from an initial position to a final position in an environment with obstacles. Recent researches do not just aim to find feasible paths but to find paths that are optimal in respect to time, distance and safety of the robots. Optimization based techniques have been proposed to solve this problem but some of them used techniques that may converge to local minimum and they seldom consider the speed of the technique. Hence this paper presents a fast and global motion planning algorithm for a mobile robot in a known environment with static obstacles. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. The coordinate values when generated by the customized algorithm are passed to the PSO algorithm which uses them to determine the shortest path between the two given end positions. We perform our experiments using four different environments with population sizes 100, 50, 20 and 10 in a 10 × 10 grid and our results are favorable.
KeywordsMotion planning Optimization Particle swarm optimization Algorithm Robotics
Covenant University is acknowledged for sponsoring this research.
All the authors contributed immensely to this research. The paper is a product of optimization sub cluster of the Department of Mathematics, Covenant University, Ota.
Compliance with ethical standards
Conflicts of interest
The authors declare no conflict of interest.
- 2.Denny, J., Greco, E., Thomas, S. L., & Amato, N. M. (2014). MARRT: Medial axis biased rapidly-exploring random trees. In Proceedings of IEEE international conference of robotics automation (ICRA) (pp. 90–97). Hong Kong, China.Google Scholar
- 3.Ekenna, C., Jacobs, S. A., Thomas, S. L., & Amato N. M. (2013). Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism. In: Proceedings of IEEE international conference on intelligent robotics systems (IROS), Tokyo, Japan.Google Scholar
- 6.Lien, J.-M., Bayazit, O. B., Sowell, R.-T., Rodriguez, S., & Amato, N. M. (2004). Shepherding behaviors. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 4159–4164).Google Scholar
- 8.Paquet, U., & Engelbrecht, A. P. (2003). Training support vector machines with particle swarms. In Proceedings of international joint conference on neural networks (IJCNN) conference (pp. 1593–1598).Google Scholar
- 9.Reif, J. H. (1979). Complexity of the mover’s problem and generalizations. In: Proceedings of IEEE symposium on foundations of computer science (FOCS) (pp. 421–427). San Juan, Puerto Rico.Google Scholar
- 10.Song, G., & Amato, N. M. (2001). Using motion planning to study protein folding pathways. In Proceedings of international conference Computer Molecular Biology (RECOMB) (pp. 287–296).Google Scholar
- 11.van den Bergh, F. (2002). An analysis of particle swarm optimizers (pp. 15–30). Ph.D. Thesis, Department of Computer Science, University of Pretoria.Google Scholar
- 12.Venu, G. G., & Ganesh, K. V. (2003). Evolving digital circuits using particle swarm. In Proceedings of international joint conference on neural networks (IJCNN) conference (pp. 468–471).Google Scholar