Research on path planning algorithm of unmanned ground platform based on reinforcement learning

Abstract

Path planning algorithm is the basis of unmanned ground platform to realize unmanned driving function. Traditional path planning algorithms mostly regard path planning as a geometric problem, which has great limitations on the work of unmanned platforms in the current complex environment. The reinforcement learning algorithm focuses on online planning and has the advantage of continuing to explore and find better solutions on the basis of effective actions. This paper studies path planning of unmanned ground platform based on reinforcement learning method. Aiming at the problems of low flexibility and slow convergence of the current reinforcement learning method in path planning, this paper improves the Q-learning algorithm based on the reinforcement learning algorithm and conducts simulation experiments and analyzes the experimental results. The analysis shows that the path planning algorithm of unmanned ground platform based on reinforcement learning has obvious advantages in performance.