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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2010 - 11


Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning

MASEHIAN, E. See more information about MASEHIAN, E. on SCOPUS See more information about MASEHIAN, E. on IEEExplore See more information about MASEHIAN, E. on Web of Science, SEDIGHIZADEH, D. See more information about SEDIGHIZADEH, D. on SCOPUS See more information about SEDIGHIZADEH, D. on SCOPUS See more information about SEDIGHIZADEH, D. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (12,322 KB) | Citation | Downloads: 1,510 | Views: 5,873

Author keywords
swarm robotic, infrared, AMiR, modulation methods

References keywords
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

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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

[1] 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.

[2] 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.

[3] 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.

[4] 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 45]

[5] 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 59]

[6] 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 85]

[7] 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 2525] [SCOPUS Times Cited 3264]

[8] 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.

[9] 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 26458]

[10] 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.

[11] Y. Shi and R. Eberhart, "Particle swarm optimization with fuzzy adaptive inertia weight", in Proc. Workshop on Particle Swarm Optimization, Indianapolis, 2001.

[12] 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 44] [SCOPUS Times Cited 42]

[13] 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.

[14] 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.
[CrossRef] [SCOPUS Times Cited 8]

[15] 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 13]

References Weight

Web of Science® Citations for all references: 29,036 TCR
SCOPUS® Citations for all references: 3,516 TCR

Web of Science® Average Citations per reference: 1,815 ACR
SCOPUS® Average Citations per reference: 220 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 2021-03-04 05:53 in 55 seconds.

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