Deep Reinforcement Learning with Heuristic Corrections for UGV Navigation

Abstract

Mapless navigation for mobile Unmanned Ground Vehicles (UGVs) using Deep Reinforcement Learning (DRL) has attracted significantly rising attention in robotic and related research communities. Collision avoidance from dynamic obstacles in unstructured environments, such as pedestrians and other vehicles, is one of the key challenges for mapless navigation. This paper proposes a DRL algorithm based on heuristic correction learning for autonomous navigation of a UGV in mapless configuration. We use a 24-dimensional lidar sensor, and merge the target position information and the speed information of the UGV as the input of the reinforcement learning agent. The actions of the UGV are produced as the output of the agent. Our proposed algorithm has been trained and evaluated in both static and dynamic environments. The experimental result shows that our proposed algorithm can reach the target in less time with shorter distances under the premise of ensuring safety than other algorithms. Especially, the success rate of our proposed algorithm is 2.05 times higher than the second effective algorithm and the trajectory efficiency is improved by 24% in the dynamic environment. Finally, our proposed algorithm is deployed on a real robot in the real-world environment to validate and evaluate the algorithm performance. Experimental results show that our proposed algorithm can be directly applied to real robots robustly.