Rough-Terrain Path Planning Based on Deep Reinforcement Learning

Road undulations have a significant impact on path lengths and energy consumption, so rough-terrain path planning for unmanned vehicles is of great research importance for performing more tasks with limited energy. This paper proposes a Deep Q-Network (DQN)-based path-planning method, which shapes the reward by introducing a slope penalty function and a terrain penalty function. For the problem of the low exploration efficiency of the ε-greedy strategy, a hybrid exploration strategy combining stochastic exploration and the A* algorithm is proposed, after which the agent is trained on rough terrain. The results show that the algorithm can efficiently plan energy-saving paths, converge quickly, and compared with the traditional A* algorithm and RRT algorithm, performs better under three-dimensional terrain and can choose paths more rationally.