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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


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",
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publisher="Zhejiang University Press & Springer",

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%T Multi-objective robot motion planning using a particle swarm optimization model
%A Ellips Masehian
%A Davoud Sedighizadeh
%J Journal of Zhejiang University SCIENCE C
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910525

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
VL - 11
IS - 8
SP - 607
EP - 619
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910525

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


[1]Asano, T., Asano, T., Guibas, L., Hershberger, J., Imai, H., 1985. Visibility-Polygon Search and Euclidean Shortest Path. Proc. 26th Symp. on Foundations of Computer Science, p.155-164.

[2]Bhattacharya, P., Gavrilova, M., 2008. Path planning with the required minimum clearance using the Voronoi diagram methodology. IEEE Rob. Autom. Mag., 15(2):58-66.

[3]Canny, J.F., 1985. A Voronoi Method for the Piano-Movers Problem. Proc. IEEE Int. Conf. on Robotics and Automation, 2:530-535.

[4]Canny, J.F., 1987. A New Algebraic Method for Robot Motion Planning and Real Geometry. Proc. 28th IEEE Annual Symp. on Foundations of Computer Science, p.39-48.

[5]Canny, J.F., 1988. The Complexity of Robot Motion Planning. MIT Press, Cambridge, MA, USA.

[6]Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G., 2003. Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput., 7(3):289-304.

[7]Cen, Y., Wang, L., Zhang, H., 2007. Real-Time Obstacle Avoidance Strategy for Mobile Robot Based on Improved Coordinating Potential Field with Genetic Algorithm. IEEE Int. Conf. on Control Applications, p.415-419.

[8]Chang, H.C., Liu, J.S., 2009. High-Quality Path Planning for Autonomous Mobile Robots with η3-Splines and Parallel Genetic Algorithms. IEEE Int. Conf. on Robotics and Biomimetics, p.1671-1677.

[9]Chen, X., Li, Y., 2006. Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization. Proc. IEEE Int. Conf. on Mechatronics and Automation, p.1722-1727.

[10]Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S., 2005. Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, Boston.

[11]Davidor, Y., 1990. Robot Programming with a Genetic Algorithm. Proc. IEEE Int. Conf. on Computer Systems and Software Engineering, p.186-191.

[12]Fujimura, K., 1996. Path planning with multiple objectives. IEEE Rob. Autom. Mag., 3(1):33-38.

[13]Gao, M., Xu, J., Tian, J., Wu, H., 2008. Path Planning for Mobile Robot Based on Chaos Genetic Algorithm. Proc. Int. Conf. on Natural Computation, p.409-413.

[14]Ghorbani, A., Shiry, S., Nodehi, A., 2009. Using Genetic Algorithm for a Mobile Robot Path Planning. Proc. Int. Conf. on Future Computer and Communication, p.164-166.

[15]Gong, D., Lu, L., Li, M., 2009. Robot Path Planning in Uncertain Environments Based on Particle Swarm Optimization. Proc. IEEE Congress on Evolutionary Computation, p.2127-2134.

[16]Hassan, R., Cohanim, B., de Weck, O., 2004. A Comparison of Particle Swarm Optimization and the Genetic Algorithm. Proc. 46th Structures, Structural Dynamics and Materials Conf., p.1-13.

[17]Hwang, Y.K., Ahuja, N., 1992. Gross motion planning—a survey. ACM Comput. Surv., 24(3):219-291.

[18]Janabi-Sharifi, F., Vinke, D., 1993. Integration of the Artificial Potential Field Approach with Simulated Annealing for Robot Path Planning. Proc. IEEE Int. Symp. on Intelligent Control, p.536-541.

[19]Kang, D.O., Kim, S.H., Lee, H., Bien, Z., 2001. Multiobjective navigation of a guide mobile robot for the visually impaired based on intention inference of obstacles. Auton. Rob., 10(2):213-230.

[20]Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M., 1996. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Rob. Autom., 12(4):566-580.

[21]Keil, J.M., Sack, J.R., 1985. Minimum Decomposition of Polygonal Objects. In: Toussaint, G.T. (Ed.), Computational Geometry. North-Holland, Amsterdam, p.197-216.

[22]Kennedy, J., Eberhart, R.C., 1995. Particle Swarm Optimization. Proc. Int. Conf. on Neural Networks, p.1942-1948.

[23]Khatib, O., 1986. Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Rob. Res., 5(1):90-99.

[24]Kim, J., Pearce, R.A., Amato, N.M., 2003. Extracting Optimal Paths from Roadmaps for Motion Planning. Proc. IEEE Int. Conf. on Robotics and Automation, p.2424-2429.

[25]Latombe, J.C., 1991. Robot Motion Planning. Kluwer Academic Publishers, Boston.

[26]LaValle, S.M., 1998. Rapidly-Exploring Random Trees: a New Tool for Path Planning. Technical Report, TR 98-11, Computer Science Department, Iowa State University, USA.

[27]Li, G., Shi, H., 2008. Path Planning for Mobile Robot Based on Particle Swarm Optimization. Proc. Control and Decision Conf., p.3290-3294.

[28]Li, Q., Tong, X., Xie, S., Zhang, Y., 2006. Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm. Proc. 6th Int. Conf. on Hybrid Intelligent Systems, p.53-58.

[29]Liu, G., Yuan, J.P., Xu, Y.S., 2008. Multi-Objective Optimal Trajectory Planning of Space Robot Using Particle Swarm Optimization. Proc. Int. Symp. on Neural Networks, p.171-179.

[30]Lozano-Pérez, T., Wesley, M.A., 1979. An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM, 22(10):560-570.

[31]Masehian, E., Amin-Naseri, M.R., 2004. A Voronoi diagram-visibility graph-potential field compound algorithm for robot path planning. J. Rob. Syst., 21(6):275-300.

[32]Masehian, E., Sedighizadeh, D., 2007. Classic and Heuristic Approaches in Robot Motion Planning: a Chronological Review. Proc. World Academy of Science, Engineering and Technology, p.101-106.

[33]Min, H.Q., Zhu, J.H., Zheng, X.J., 2005. Obstacle Avoidance with Multiobjective Optimization by PSO in Dynamic Environment. Proc. Int. Conf. on Machine Learning and Cybernetics, p.2950-2956.

[34]Mishra, S.K., 2006. Repulsive Particle Swarm Method on Some Difficult Test Problems of Global Optimization. Available from http://mpra.ub.uni-muenchen.de/1742/ [Accessed on July 16, 2009].

[35]Naderan-Tahan, M., Manzuri-Shalmani, M.T., 2009. Efficient and Safe Path Planning for a Mobile Robot Using Genetic Algorithm. Proc. IEEE Congress on Evolutionary Computation, p.2091-2097.

[36]Park, J.B., Lee, K.S., Shin, J.R., Lee, K.Y., 2005. A particle swarm optimization for economic dispatch with no smooth cost functions particle swarm optimization for economic dispatch with any smooth cost functions. IEEE Trans. Power Syst., 20(1):34-42.

[37]Qin, Y.Q., Sun, D.B., Li, N., Cen, Y.G., 2004. Path Planning for Mobile Robot Using the Particle Swarm Optimization with Mutation Operator. Proc. Int. Conf. on Machine Learning and Cybernetics, p.2473-2478.

[38]Raja, P., Pugazhenthi, S., 2009. Path Planning for Mobile Robots in Dynamic Environments Using Particle Swarm Optimization. Proc. Int. Conf. on Advances in Recent Technologies in Communication and Computing, p.401-405.

[39]Rekik, C., Jallouli, M., Derbel, N., 2009. Optimal Trajectory of a Mobile Robot by a Genetic Design Fuzzy Logic Controller. Proc. Int. Conf. on Advances in Computational Tools for Engineering and Applications, p.107-111.

[40]Saska, M., Macas, M., Preucil, L., Lhotska, L., 2006. Robot Path Planning Using Particle Swarm Optimization of Ferguson Splines. Proc. IEEE Conf. on Emerging Technologies and Factory Automation, p.833-839.

[41]Sedighizadeh, D., Masehian, E., 2009. A New Taxonomy for Particle Swarm Optimization (PSO). Proc. 10th Int. Conf. on Automation Technology, p.317-322.

[42]Shi, Y., Eberhart, R., 2001. Particle Swarm Optimization with Fuzzy Adaptive Inertia Weight. Proc. Workshop on Particle Swarm Optimization, p.101-106.

[43]Shibata, T., Fukuda, T., 1993. Intelligent Motion Planning by Genetic Algorithm with Fuzzy Critic. Proc. IEEE Int. Conf. on Intelligent Control, p.565-570.

[44]Solano, J., Jones, D.I., 1993. Generation of Collision-Free Paths: a Genetic Approach. Proc. IEEE Colloquium on Genetic Algorithms for Control Systems Engineering, p.5/1-5/6.

[45]Wilson, L.A., Moore, M.D., Picarazzi, J.P., Miquel, S.D.S., 2004. Parallel Genetic Algorithm for Search and Constrained Multi-Objective Optimization. Proc. 18th Int. Parallel and Distributed Processing Symp., p.165.

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