A Hybrid Approach Based on ACO and Ga for Multi Objective Mobile Robot Path Planning

Abstract

In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.