A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

Abstract

Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are introduced into the IQL algorithm, namely the normal mode, the distortion mode, and the optimization mode. The normal mode operates according to the standard Q-learning procedures. The distortion mode distorts the Q-values of states around dynamic obstacles to facilitate avoidance, while the optimization mode is employed to overcome the local minimum problem. The efficacy of the IQL algorithm is assessed through a series of comparative studies involving fourteen navigation environments, each with distinct obstacle layouts and types. Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. The proposed IQL algorithm exhibits a lower collision rate and a higher success rate when compared to dynamic window approach, influence zone and inflated A*.