基于最大空闲时间的分布式巡逻机器人数量优化

针对多机器人巡逻问题,提出基于全局平均最大空闲时间的分布式巡逻算法,确保每个巡逻顶点在一定时间内被机器人访问.在算法执行过程中,每个机器人利用共享信息估算全局平均最大空闲时间.机器人在当前顶点收集的信息用于决策、选择下一个被机器人访问的顶点.再根据全局平均最大空闲时间的大小估算在巡逻任务过程中多机器人团队的表现,确定完成此巡逻任务所需的最优机器人数量.仿真实验表明文中算法的收敛速度较快,全局平均最大空闲时间值较小,多机器人巡逻任务完成效果较佳.

Team Size Optimization for Distributed Patrol of Multi-robot Systems Based on Maximum Idle Time

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.