An Optimal Path Planning for Multiple Mobile Robots Using AIS and GA: A Hybrid Approach

Abstract

Design of proficient control algorithms for mobile robot navigation in an unknown and changing environment, with obstacles and walls is a complicated task. The objective for building the intelligent planner is to plan actions for multiple mobile robots to coordinate with others and to achieve the global goal by avoiding static and dynamic obstacles. This paper demonstrates a hybrid method of two optimization techniques that are Artificial Immune System AIS and Genetic Algorithm GA. The capability of overcoming the shortcomings of individual algorithms without losing their advantage makes the hybrid techniques superior to the stand-alone ones. The main objective behind this is to improvise the result of a path planning approach than done on AIS and GA separately. The hybridization includes two phases; in first enhancing the local searching ability by AIS and secondly to add stochasticity, instead of choosing random population, the last generation of AIS will be accepted as input to the next process of GA in the hybrid AIS-GA. From the result and observations, it can be inferred that the proposed algorithm is able to efficiently explore the unknown environment by learning from past behavior towards reaching the target. The result obtained from the hybrid algorithm is compared over AIS and GA and found to be more efficient in terms of convergence speed and the time taken to reach at the target, making it a promising approach for solving the mobile robot path planning problem.