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基于改进深度Q网络的机器人持续监测路径规划

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持续监测问题指的是通过规划移动机器人在路网中的巡逻路线,从而对路网环境实施长期监测,以实现保障环境安全的目的。环境中的待监测点通常受到最大允许监测周期(重访周期)的限制,并且最优的监测路径不应具有固定的周期,否则监测过程容易被恶意入侵者针对性地破坏。针对上述问题,提出一种基于改进深度Q网络(Deep Q Networks,DQN)的机器人监测路径规划算法。改进DQN的决策方法,使机器人获得一条监测频率高、安全性好(防止被智能入侵的能力)、非固定周期的监测路径。仿真实验结果表明:所提算法可以高效地覆盖所有待监测节点;与传统的DQN算法相比,该算法不会使监测陷入周期性的循环路径之中,增强了系统的抗入侵能力。
Robot Path Planning for Persistent Monitoring Based on Improved Deep Q Networks
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.

persistent monitoringreinforcement learningpath planningmobile robot

王霄龙、陈洋、胡棉、李旭东

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武汉科技大学 冶金自动化与检测技术教育部工程研究中心,湖北 武汉 430081

武汉科技大学 机器人与智能系统研究院,湖北 武汉 430081

持续监测 强化学习 路径规划 移动机器人

国家自然科学基金国家自然科学基金

6217326262073250

2024

兵工学报
中国兵工学会

兵工学报

CSTPCD北大核心
影响因子:0.735
ISSN:1000-1093
年,卷(期):2024.45(6)
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