Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

Abstract

Abstract This paper proposes a new novel approach to find an optimal path for a mobile robot in a two‐dimensional environment. Finding the optimal path is done using the Bees Algorithm (BA) with the Q‐Learning Algorithm. A new method to build the initial population is proposed to find the initial population regardless of the number and location of obstacles in the environment. Q‐Learning is implemented as a local search function of the BA. The hybridization of the BA and the Q‐Learning aims to find the optimal path with a fewer number of iterations of the BA. This method takes advantage of the BA to solve the problem without constraints and the sterilization in the Q‐Learning to find the shortest path. The experiment is run on some different maps to validate the proposed method in the static and dynamic case. The experimental results show the robustness and effectiveness of the proposed method in finding the optimal path. The comparison is executed to view the superiority of this method in finding the shortest path in the comparison of the results of other algorithms.