Path Planning Technology of Unmanned Vehicle Based on Improved Deep Reinforcement Learning

Kai Zhang, Luhe Wang, Jinwen Hu, Zhao Xu, Chubing Guo

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

As the basic problem of unmanned vehicle navigation control, path planning has been widely studied. Reinforcement learning (RL) has been found an effective way of path optimization for the highly nonlinear and unmodeled dynamics. However, the RL based methods suffer from the "dimension disaster"under the high-dimension state spaces. In this paper, the path planning of an unmanned vehicle with collision avoidance is considered, and an improved Deep Q-Network (DQN) algorithm is proposed to reduce the computation load in the high-dimension state space. First, the states, actions and rewards are determined based on the task requirement, and a smoothing function is defined as an additional penalty term to modify the basic reward function. Then, the two-dimension grid of the state space is mapped to a gray image, which is applied as the input of a neural network, i.e., the Q-Network. Finally, simulation results show that the modified DQN algorithm is more stable and the fluctuation frequency is significantly reduced.

源语言英语
主期刊名Proceedings of the 40th Chinese Control Conference, CCC 2021
编辑Chen Peng, Jian Sun
出版商IEEE Computer Society
8392-8397
页数6
ISBN(电子版)9789881563804
DOI
出版状态已出版 - 26 7月 2021
活动40th Chinese Control Conference, CCC 2021 - Shanghai, 中国
期限: 26 7月 202128 7月 2021

出版系列

姓名Chinese Control Conference, CCC
2021-July
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议40th Chinese Control Conference, CCC 2021
国家/地区中国
Shanghai
时期26/07/2128/07/21

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