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Multi-Objective PSO- and NPSO-based Algorithms for Robot Path PlanningMASEHIAN, E. , SEDIGHIZADEH, D.
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swarm robotic, infrared, AMiR, modulation methods
optimization(11), swarm(10), robot(7), planning(7), path(5)
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About this article
Date of Publication: 2010-11-30
Volume 10, Issue 4, Year 2010, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.04011
Web of Science Accession Number: 000284782700011
SCOPUS ID: 78649718263
In this paper two novel Particle Swarm Optimization (PSO)-based algorithms are presented for robot path planning with respect to two objectives, the shortest and smoothest path criteria. The first algorithm is a hybrid of the PSO and the Probabilistic Roadmap (PRM) methods, in which the PSO serves as the global planner whereas the PRM performs the local planning task. The second algorithm is a combination of the New or Negative PSO (NPSO) and the PRM methods. Contrary to the basic PSO in which the best position of all particles up to the current iteration is used as a guide, the NPSO determines the most promising direction based on the negative of the worst particle position. The two objective functions are incorporated in the PSO equations, and the PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. Both the PSO+PRM and NPSO+PRM algorithms are compared with the pure PRM method in path length and runtime. The results showed that the NPSO has a slight advantage over the PSO, and the generated paths are shorter and smoother than those of the PRM and are calculated in less time.
|References|||||Cited By «-- Click to see who has cited this paper|
| H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki and S. Thrun, "Principle of Robot Motion: Theory, Algorithms, and Application," MIT Press, Cambridge, 2005, ISBN 0-262-03327-5.
 E. Masehian, and D. Sedighizadeh, "Classic and heuristic approaches in robot motion planning-a chronological review", Proc. of World Academy of Science, Engineering and Technology, Vol. 23, pp. 101-106, 2007.
 Q. Yuan-Qing, S. De-Bao, L. Ning and C. Yi-Gang, "Path planning for mobile robot using the particle swarm optimization with mutation operator", in Proc. Int. Conf. on Machine Learning and Cybernetics, pp. 2473 - 2478, 2004.
 W. Li, L. Yushu, D. Hongbin, and X. Yuanqing, "Obstacle-avoidance path planning for soccer robots using particle swarm optimization", in Proc. IEEE Int. Conf. on Rob. and Biomimetics (ROBIO) pp. 1233-1238, 2006.
[CrossRef] [SCOPUS Times Cited 31]
 C. Xin and L. Yangmin, "Smooth path planning of a mobile robot using stochastic particle swarm optimization" in Proc. IEEE on Mechatronics and Automation, pp. 1722-1727, 2006.
[CrossRef] [SCOPUS Times Cited 43]
 Yang, C. and Simon, D., "A new particle swarm optimization technique", in Proc. IEEE Int. Conf. on Systems Engineering, pp. 164- 169, 2005.
 L. Kavraki, P. Svestka, J.C. Latombe, and M. Overmars, "Probabilistic roadmaps for path planning in high-dimensional configuration spaces", IEEE Trans. Robot. Autom. Vol 12, No. 4, pp. 566-580, 1996.
[CrossRef] [Web of Science Times Cited 1704] [SCOPUS Times Cited 2304]
 R. Hassan, B. Cohanim, and O. de Weck, "A comparison of particle swarm optimization and the genetic algorithm", American Institute of Aeronautics and Astronautics, 2004.
 J. Kennedy, and R.C. Eberhart, "Particle swarm optimization", in Proc. IEEE Int. Conf. on Neural Networks, pp. 1942-1948, 1995.
[CrossRef] [Web of Science Times Cited 18098] [SCOPUS Record]
 D. Sedighizadeh and E. Masehian, "A new taxonomy for particle swarm optimization (PSO)", in Proc. 10th International Conference on Automation Technology, National Cheng Kung University, Tainan, Taiwan, pp. 317-322, 2009.
 Y. Shi and R. Eberhart, "Particle swarm optimization with fuzzy adaptive inertia weight", in Proc. Workshop on Particle Swarm Optimization, Indianapolis, 2001.
 K., Fujimura, "Path planning with multiple objectives", J. of IEEE Robotics and Automation Society, vol.3. No.1, pp. 33-38, 1996.
[CrossRef] [Web of Science Times Cited 31]
 H. Q. Min, J. H. Zhu, and X.J. Zheng, "Obstacle avoidance with multi-objective optimization by PSO in dynamic environment", in Proc. IEEE Int. Conf. Machine Learning and Cyber., Vol. 5, pp. 2950-2956, 2005.
 L. Gang, Y. Jianping, and X. Yangsheng, "Multi-objective optimal trajectory planning of space robot using particle swarm optimization", vol. 5264, Proc. of Int. symp. On Neural Networks, pp. 171-179, 2008.
 D. O. Kang, S. H. Kim, H. Lee, and Z. Bien, "Multi objective navigation of a guide mobile robot for the visually impaired based on intention inference of obstacles", J. of Autonomous Robots, Vol. 10, No. 2, 2001.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]
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