|4/2010 - 11|
Multi-Objective PSO- and NPSO-based Algorithms for Robot Path PlanningMASEHIAN, E. , SEDIGHIZADEH, D.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (12,322 KB) | Citation | Downloads: 1,428 | Views: 4,927|
swarm robotic, infrared, AMiR, modulation methods
optimization(11), swarm(10), robot(7), planning(7), path(5)
Blue keywords are present in both the references section and the paper title.
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 38]
 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 51]
 Yang, C. and Simon, D., "A new particle swarm optimization technique", in Proc. IEEE Int. Conf. on Systems Engineering, pp. 164- 169, 2005.
[CrossRef] [SCOPUS Times Cited 76]
 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 2056] [SCOPUS Times Cited 2714]
 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 22100]
 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 34] [SCOPUS Times Cited 36]
 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]
Web of Science® Citations for all references: 24,199 TCR
SCOPUS® Citations for all references: 2,926 TCR
Web of Science® Average Citations per reference: 1,512 ACR
SCOPUS® Average Citations per reference: 183 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2019-03-24 20:03 in 72 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.