A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning

  • Xueyuan Wang 1. School of Electrical Engineering Southwest Jiaotong University Chengdu 610031, P.R. China 2. School of Information Engineering Southwest University of Science and Technology MianYang 621010, P.R.China
  • Gexiang Zhang School of Electrical Engineering Southwest Jiaotong University Chengdu 610031, P.R. China
  • Junbo Zhao School of Electrical Engineering Southwest Jiaotong University Chengdu 610031, P.R. China
  • Haina Rong School of Electrical Engineering Southwest Jiaotong University Chengdu 610031, P.R. China
  • Florentin Ipate Faculty of Mathematics and Computer Science University of Bucharest Academiei 14, Bucharest, Romania
  • Raluca Lefticaru Faculty of Mathematics and Computer Science University of Bucharest Academiei 14, Bucharest, Romania

Abstract

To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO.

References

[1] G. Păun, G. Rozenberg, A. Salomaa (eds.) (2010); The Oxford handbook of membrane computing, Oxford University Press.

[2] G. Păun (2007); Tracing some open problems in membrane computing, Rom J Inform Sci Tech, ISSN 1453-8245, 10(4): 303–314.

[3] G. Zhang, J. Cheng, M. Gheorghe, Q. Meng (2013); A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems, Appl Soft Comput, ISSN 1568-4946, 13(3):1528-1542.

[4] T.Y. Nishida (2004); An application of P system: a new algorithm for NP-complete optimization problems. Proc 8th WMCSCI, 109–112.

[5] G.X. Zhang, M. Gheorghe, L.Q. Pan,; M.J. Pérez-Jiménez (2014); Evolutionary membrane computing: a comprehensive survey and new results, Inform Sci, ISSN: 0020-0255, 279: 528- 551.

[6] L. Huang, X. He, N. Wang, Y. Xie (2007); P systems based multi-objective optimization algorithm, Prog Nat Sci, ISSN 1002-0071, 17(4): 458-465.

[7] G.X. Zhang, M. Gheorghe, Y. Li (2012); A membrane algorithm with quantum-inspired subalgorithms and its application to image processing, Nat Comput, ISSN 1567-7818, 11(3): 701-717.

[8] G.X. Zhang, J.X. Cheng, M. Gheorghe (2014); Dynamic behavior analysis of membraneinspired evolutionary algorithms, International Journal of Computers Communications & Control, ISSN 1841-9836, 9(2): 227-242.

[9] J. Kennedy, R. Eberhart (1995); Particle swarm optimization, Proc ICNN, 4: 1942-1948.

[10] G.X. Zhang, F. Zhou, X.L. Huang, J.X. Cheng, M. Gheorghe, F. Ipate, R. Lefticaru (2012); A novel membrane algorithm based on particle swarm optimization for solving broadcasting problems, J Univers Comput Sci, ISSN 0948-6968 18(13): 1821-1841.

[11] J. Xiao, Y. Huang, Z. Cheng, J. He, Y. Niu (2014); A hybrid membrane evolutionary algorithm for solving constrained optimization problems, Optik, ISSN 0030-4026, 125(2): 897-902.

[12] T. Lozano-Pérez,M.A. Wesley (1979); An algorithm for planning collision-free paths among polyhedral obstacles, Commun ACM, ISSN 0001-0782, (22)10: 560–570.

[13] H. Gao, J. Cao (2012); Membrane quantum particle swarm optimisation for cognitive radio spectrum allocation, Int J Comput Appl Tech, ISSN 0952-8091, 43(4): 359-365.

[14] Y.K. Hwang, N. Ahuja (1992); Gross motion planning-A survey, ACM Comp Surv, 24: 219-291.

[15] A. Tuncer, M. Yildirim (2012); Dynamic path planning of mobile robots with improved genetic algorithm, Comput Electr Eng, ISSN 0045-7906, 38: 1564-1572.

[16] M.A. Garcia, O. Montiel (2009); Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation, Appl Soft Comput, ISSN 1568-4946, 9(3): 1102-1110.

[17] Z. Qidan, Y.J. Yang, Z.Y. Xing (2006); Robot path planning sased on artificial potential field approach with simulated annealing, Proc ISDA, 622-627.

[18] Y. Zhang, D.W. Gong (2013); Robot path planning in uncertain environment using multiobjective particle swarm optimization, Neurocomputing, ISSN 0925-2312, 103: 172-185.

[19] E. Masehian, D. Sedighizadeh (2007); Classic and heuristic approaches in robot motion planning-a chronological review, Proc. WASET, 101-106.

[20] W.F. Xu, C. Li, B. Liang (2008); The cartesian path planning of free-floating space robot using particle swarm optimization, Int J Adv Rob Syst, ISSN 1729-8806, 5: 301-310.

[21] D.W. Gong, J.H. Zhang (2011); Multi-objective particle swarm optimization for robot path planning in environment with danger sources, J Comput, 6(8): 1554-1561.
Published
2015-06-01
How to Cite
WANG, Xueyuan et al. A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 5, p. 732-745, june 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2030>. Date accessed: 10 aug. 2020. doi: https://doi.org/10.15837/ijccc.2015.5.2030.

Keywords

Membrane computing, evolutionary membrane computing, particle swarm optimization, variable dimensions, mobile robot path planning, membrane systems