Affiliation:
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Abstract
Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in 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, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.
Funder
National Natural Science Foundation of China
Guangxi Natural Science Foundation
Key Laboratory of AI and Information Processing (Hechi University), the Education Department of Guangxi Zhuang Autonomous Region
Innovation Project of Guangxi Graduate Education
Innovation Project of GUET Graduate Education
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
2 articles.
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