Robot Path Planning for Persistent Monitoring Based on Improved Deep Q Networks

被引：0

作者：

Wang X. ^{[1
,2
]}

Chen Y. ^{[1
,2
]}

Hu M. ^{[1
,2
]}

Li X. ^{[1
,2
]}

机构：

[1] Engineering Research Center for Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan University of Science and Technology, Hubei, Wuhan

[2] Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Hubei, Wuhan

来源：

Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 06期

关键词：

mobile robot; path planning; persistent monitoring; reinforcement learning;

D O I：

10.12382/bgxb.2023.0227

中图分类号：

学科分类号：

摘要：

Persistent monitoring refers to the long-term monitoring of road network environment by planning the patrol route of mobile robots in the road network, so as to achieve the purpose of ensuring environmental safety. The sites to be monitored in the environment are usually limited by the maximum allowable monitoring period (revisit period). A fixed monitoring period should not be set for an optimal monitoring path, otherwise, the monitoring process is easy to be destroyed by malicious intruders. To solve the above problems, a robot monitoring path planning algorithm based on improved Deep Q Networks (DQN) is proposed, the decision-making method of DQN is improved, and a monitoring path with high monitoring frequency, good security (ability to prevent intelligent intrusion) and non-fixed period is planned for robot. Simulated and experimental results show that the proposed algorithm can efficiently cover all nodes to be monitored. Compared with the traditional DQN algorithm, the proposed algorithm does not make the monitoring fall into the cyclic path, and enhances the anti-intrusion ability of the persistent monitoring system. © 2024 China Ordnance Industry Corporation. All rights reserved.

引用

页码：1813 / 1823

页数：10

共 21 条

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