Considering that in the multi-robot patrol task, it is a common phenomenon that the importance of the nodes to be visited is different. In view of the fact that most patrolling algorithms don't consider the difference in the importance of nodes, the idle time of all nodes tends to be the same, resulting in important nodes access frequency ordinary nodes and excessive access problems. This paper proposed a distributed patrolling strategy based on nodes' importance to optimize node access frequency and reduce the global average idle time. The robot calculated the idle time and importance of surrounding nodes, online decided the target node, estimated the time of reaching the target node, and informed nearby companions of the target node and estimated time of arrival. It increased the importance of the edge node in order to prevent some nodes from being over-visited, when the access frequency of the edge node was lower than the minimum access frequency, so that the robot could visit the node as soon as possible. Finally, this paper analyzed the influence of factors on the completion of continuous patrolling tasks of robots through simulation on the number of robots, changing environment, the importance of nodes and other factors. The results show that the number of visits to the most important nodes has increased significantly. Moreover, under the premise of the same patrolling environment and number of robots, the algorithm has fewer outliers and smaller average idle time, and the performance of multi-robot continuous patrolling is better.

[2021-10-18] Accepted Paper /doc/2021.06.0301/v1

[2022-02-05] Printed Article /doc/2021.06.0301/v2