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On-line Access: 2010-08-02

Received: 2009-08-23

Revision Accepted: 2010-01-07

Crosschecked: 2010-06-09

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.8 P.607-619

http://doi.org/10.1631/jzus.C0910525


Multi-objective robot motion planning using a particle swarm optimization model


Author(s):  Ellips Masehian, Davoud Sedighizadeh

Affiliation(s):  Faculty of Engineering, Tarbiat Modares University, Tehran 14115-143, Iran

Corresponding email(s):   masehian@modares.ac.ir, sedighizadeh@modares.ac.ir

Key Words:  Robot motion planning, Particle swarm optimization, Probabilistic roadmap, Genetic algorithm


Ellips Masehian, Davoud Sedighizadeh. Multi-objective robot motion planning using a particle swarm optimization model[J]. Journal of Zhejiang University Science C, 2010, 11(8): 607-619.

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author="Ellips Masehian, Davoud Sedighizadeh",
journal="Journal of Zhejiang University Science C",
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%T Multi-objective robot motion planning using a particle swarm optimization model
%A Ellips Masehian
%A Davoud Sedighizadeh
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%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910525

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T1 - Multi-objective robot motion planning using a particle swarm optimization model
A1 - Ellips Masehian
A1 - Davoud Sedighizadeh
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910525


Abstract: 
Two new heuristic models are developed for motion planning of point robots in known environments. The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and the probabilistic roadmap (PRM) method acting as a local obstacle avoidance planner. For the PSO component, new improvements are proposed in initial particle generation, the weighting mechanism, and position- and velocity-updating processes. Moreover, two objective functions which aim to minimize the path length and oscillations, govern the robot’s movements towards its goal. The PSO and PRM components are further intertwined by incorporating the best PSO particles into the randomly generated PRM. The second model combines a genetic algorithm component with the PRM method. In this model, new specific selection, mutation, and crossover operators are designed to evolve the population of discrete particles located in continuous space. Thorough comparisons of the developed models with each other, and against the standard PRM method, show the advantages of the PSO method.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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