Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning

Authors

  • Nadjib Zerrouki
    Affiliation

    Department of Electronics, Faculty of Technology, Batna-2 University, Algeria

  • Noureddine Goléa
    Affiliation

    LGEA Laboratory, Department of Electrical Engineering, Faculty of Sciences and Applied Sciences, Oum El Bouaghi University, Algeria

  • Nabil Benoudjit
    Affiliation

    Department of Electronics, Faculty of Technology, Batna-2 University, Algeria

https://doi.org/10.3311/PPee.8682

Abstract

The path-planning problem is commonly formulated to handle the obstacle avoidance constraints. This problem becomes more complicated when further restrictions are added. It often requires efficient algorithms to be solved. In this paper, a new approach is proposed where the path is described by means of Non Uniform Rational B-Splines (NURBS for short) with more additional constraints. An evolutionary technique called Particle Swarm Optimization (PSO) with three options of particles velocity updating offering three alternatives namely the PSO with inertia weight (PSO-W), the constriction factor PSO (PSO-C) and the combination of the two(PSO-WC); are used to optimize the weights of the control points that serve as parameters of the algorithm describing the path. Simulation results show how the mixture of the first two options produces a powerful algorithm, specifically (PSO-WC), in producing a compromise between fast convergence and large number of potential solution. In addition, the whole approach seems to be flexible, powerful and useful for the generation of successful smooth trajectories for robot manipulator which are independent from environment conditions.

Keywords:

robot manipulators, path planning, B-splines, particle swarm optimization

Published Online

2017-11-09

How to Cite

Zerrouki, N., Goléa, N., Benoudjit, N. “Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning”, Periodica Polytechnica Electrical Engineering and Computer Science, 61(4), pp. 337–349, 2017. https://doi.org/10.3311/PPee.8682

Issue

Section

Articles