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Due to the vastly different energy consumption between up-slope anddown-slope, a path with the shortest length on a complex off-road terrainenvironment (2.5D map) is not always the path with the least energyconsumption. For any energy-sensitive vehicle, realizing a good trade-offbetween distance and energy consumption in 2.5D path planning is significantlymeaningful. In this paper, we propose a deep reinforcement learning-based 2.5Dmulti-objective path planning method (DMOP). The DMOP can efficiently find thedesired path in three steps: (1) Transform the high-resolution 2.5D map into asmall-size map. (2) Use a trained deep Q network (DQN) to find the desired pathon the small-size map. (3) Build the planned path to the originalhigh-resolution map using a path-enhanced method. In addition, the hybridexploration strategy and reward shaping theory are applied to train the DQN.The reward function is constructed with the information of terrain, distance,and border. Simulation results show that the proposed method can finish themulti-objective 2.5D path planning task with significantly high efficiency.With similar planned paths, the speed of the proposed method is more than 100times faster than that of the A* method and 30 times faster than that of H3DMmethod. Also, simulation proves that the method has powerful reasoningcapability that enables it to perform arbitrary untrained planning tasks.

Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles