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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

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


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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
 
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Download PDF pdficon (12,322 KB) | Citation | Downloads: 1,327 | Views: 3,745

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

Abstract
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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.


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[1] Improvement of the Gravitational Search Algorithm by means of Low-Discrepancy Sobol Quasi Random-Number Sequence Based Initialization, ALTINOZ, O. T., YILMAZ, A. E., WEBER, G.-W., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 14, 2014.
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[2] Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark Functions, DENIZ ULKER, E., HAYDAR, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 13, 2013.
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[3] Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics, Hidalgo-Paniagua, Alejandro, Vega-Rodríguez, Miguel A., Ferruz, Joaquín, Expert Systems with Applications, ISSN 0957-4174, Issue , 2016.
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[4] Heuristic approaches in robot path planning: A survey, Mac, Thi Thoa, Copot, Cosmin, Tran, Duc Trung, De Keyser, Robin, Robotics and Autonomous Systems, ISSN 0921-8890, Issue , 2016.
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[5] A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning, Das, P.K., Behera, H.S., Panigrahi, B.K., Swarm and Evolutionary Computation, ISSN 2210-6502, Issue , 2016.
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[11] Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity, Das, P.K., Behera, H.S., Panigrahi, B.K., Engineering Science and Technology, an International Journal, ISSN 2215-0986, Issue 1, Volume 19, 2016.
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[14] Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach, Hidalgo-Paniagua, Alejandro, Vega-Rodríguez, Miguel A., Ferruz, Joaquín, Pavón, Nieves, Soft Computing, ISSN 1432-7643, Issue 4, Volume 21, 2017.
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[15] MOSFLA-MRPP: Multi-Objective Shuffled Frog-Leaping Algorithm applied to Mobile Robot Path Planning, Hidalgo-Paniagua, Alejandro, Vega-Rodríguez, Miguel A., Ferruz, Joaquín, Pavón, Nieves, Engineering Applications of Artificial Intelligence, ISSN 0952-1976, Issue , 2015.
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[17] Modified Genetic Algorithm based on A* Algorithm of Multi Objective Optimization for Path Planning, Oleiwi, Bashra K., Roth, Hubert, Kazem, Bahaa I., Journal of Automation and Control Engineering, ISSN 2301-3702, Issue 4, Volume 2, 2014.
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[18] PSO based path planner of an autonomous mobile robot, Deepak, B., Parhi, Dayal, Open Computer Science, ISSN 2299-1093, Issue 2, Volume 2, 2012.
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[19] Adaptive Charged System Search Approach to Path Planning for Multiple Mobile Robots, Precup, Radu-Emil, Petriu, Emil M., Radae, Mircea-Bogdan, Voisan, Emil-Ioan, Dragan, Florin, IFAC-PapersOnLine, ISSN 2405-8963, Issue 10, Volume 48, 2015.
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[30] PSO-AG: A Multi-Robot Path Planning and obstacle avoidance algorithm, Bilbeisi, Ghaith, Al-Madi, Nailah, Awad, Fahed, 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), ISBN 978-1-4799-7442-9, 2015.
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