An Adaptive Multi-Objective Artificial Bee Colony Algorithm for Multi-Robot Path Planning

Abstract

This paper discusses an optimal path planning algorithm based on an Adaptive Multi Objective Artificial Bee Colony Algorithm (AMOABC) for three case studies. First case, two robots wants to reach the different target with two objectives; first objective is to find the minimum distance that is needed by the robot from the start position to the target while the second objective is to find the maximum distance between the paths of the two robots. The second case is to find the optimal path with shortest and smoothest path for the three and four robot. The last one, finding the shortest path for five robots without any collision between them with smoothest and shortest time. The results show that the AMOABC has a better ability to get away from local optimums with a quickest convergence than MOABC. 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