Optimal path planning based on goal heuristics IQLA for mobile robots

Path planning is an indispensable part of the application of intelligent mobile robots. Q-learning is a type of reinforcement learning without prior knowledge and is currently a popular path planning method. Although there are numerous research articles on Q-learning for mobile robot path planning, the difficulties of stability and convergence speed of its path planning can hinder the effectiveness of mobile robot applications in reality. This paper presents an improved Q-learning algorithm incorporating the A* algorithm (IQLA). First, a goal heuristic function is introduced to perform an initialisation operation on the Q-table, which guides the intelligent body to move toward the target for accelerating the convergence of the algorithm. Second, an adaptive exploration factor is proposed to improve the action selection strategy, which balances the exploration and exploitation of the environment via intelligence. Furthermore, an adaptive distance search range is proposed to accelerate the convergence of the algorithm. Finally, the A* heuristic evaluation function with weights is incorporated into the Q-learning reward and punishment functions to guide the algorithm to quickly converge to the optimum. This paper validates the performance and effectiveness of the IQLA algorithm over comparison algorithms for mobile robot path planning through simulation and comparison experiments.