An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

  • Nizar Hadi Abbas, Dr College of Engineering-University of Baghdad
  • Jaafer Ahmed Abdulsaheb College of Engineering-University of Baghdad
Keywords: multi-robot system, path planning, multi-objective approaches, adaptive multi-objective particle swarm optimization, danger zones.

Abstract

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

 

 

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Published
2016-07-01
How to Cite
Abbas, N. and Abdulsaheb, J. (2016) “An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning”, Journal of Engineering, 22(7), pp. 164-181. Available at: http://joe.uobaghdad.edu.iq/index.php/main/article/view/195 (Accessed: 6April2020).