A robust MLTD3 path planning algorithm in unknown environments

Abstract

We explore the problem of path planning for mobile robots in unknown environments using deep reinforcement learning (DRL). This paper proposes a multi-layer long short-term memory twin delayed deep deterministic policy gradient (MLTD3) algorithm for unknown environments. First, we introduce multi-layer long short-term memory (LSTM) networks to the actor network of the twin delayed deep deterministic policy gradient (TD3), to capture rich historical information to alleviate local optimal solutions in local path planning. Secondly, Poisson coding is employed in the state space to deal with the uncertainty of environments, so that the algorithm can discern the relationship within long sequence information. Then novel extrinsic and intrinsic reward functions are designed to avoid the dynamic obstacles in environments. Finally, the performance of the proposed algorithm is verified through simulations in ROS Gazebo and physical experiments in an unknown environment.