Due to the energy-consumption efficiency between up-slope and down-slope is
hugely different, a path with the shortest length on a complex off-road terrain
environment (2.5D map) is not always the path with the least energy
consumption. For any energy-sensitive vehicles, realizing a good trade-off
between distance and energy consumption on 2.5D path planning is significantly
meaningful. In this paper, a deep reinforcement learning-based 2.5D
multi-objective path planning method (DMOP) is proposed. The DMOP can
efficiently find the desired path with three steps: (1) Transform the
high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q
network (DQN) to find the desired path on the small-size map. (3) Build the
planned path to the original high-resolution map using a path enhanced method.
In addition, the imitation learning method and reward shaping theory are
applied to train the DQN. The reward function is constructed with the
information of terrain, distance, border. Simulation shows that the proposed
method can finish the multi-objective 2.5D path planning task. Also, simulation
proves that the method has powerful reasoning capability that enables it to
perform arbitrary untrained planning tasks on the same map.

该论文提出了一种基于深度强化学习的 2.5D 多目标路径规划方法，包括将高分辨率 2.5D 地图转换为小规模地图，使用深度 Q 网络（DQN）在小规模地图上找到期望路径，最后使用路径增强方法将规划路径建立到原始高分辨率地图上，模拟结果表明该方法能够有效完成 2.5D 路径规划任务，并具有良好的推理能力。