Abstract：Aiming at multi-robot patrol problems, a distributed patrol algorithm based on estimated global maximum idleness(EGMI) is proposed to ensure that each patrol vertex can be visited by robots in a certain period of time. In the execution process of algorithm, the global average maximum idle time is estimated using the shared information by each robot, and the next target point to be visited is decided and selected by the robot at the current vertex combining the information collected. Then, performance of the multi-robot team during the patrol task is evaluated according to the global mean maximum idle time. Thus, the optimal number of robots required to complete the patrol task can be obtained. Simulation experiments show that EGMI produces a higher convergence speed and a lower global average maximum idle time. A better result of completing the multi-robot patrol task is achieved.
赵云涛, 李宗刚, 杜亚江. 基于最大空闲时间的分布式巡逻机器人数量优化[J]. 模式识别与人工智能, 2020, 33(4): 375-382.
ZHAO Yuntao, LI Zonggang,DU Yajiang. Team Size Optimization for Distributed Patrol of Multi-robot Systems Based on Maximum Idle Time. , 2020, 33(4): 375-382.
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