Abstract:

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.

Key words: persistent monitoring, reinforcement learning, path planning, mobile robot