Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this need, this paper focuses on the common subproblem in which a team of vehicles needs to plan coordinated routes to patrol an area over iterations while minimizing temporally and spatially dependent costs. In particular, at a specific location (e.g., a vertex on a graph), we assume the cost grows linearly in expectation with an unknown rate, and the cost is reset to zero whenever any vehicle visits the vertex (representing the robot servicing the vertex). We formulate this problem in graph terminology and call it Team Orienteering Coverage Planning with Uncertain Reward (TOCPUR). We propose to solve TOCPUR by simultaneously estimating the accumulated cost at every vertex on the graph and solving a novel variant of the Team Orienteering Problem (TOP) iteratively, which we call the Team Orienteering Coverage Problem (TOCP). We provide the first mixed integer programming formulation for the TOCP, as a significant adaptation of the original TOP. We introduce a new benchmark consisting of hundreds of randomly generated graphs for comparing different methods. We show the proposed solution outperforms both the exact TOP solution and a greedy algorithm. In addition, we provide a demo of our method on a team of three physical robots in a real-world environment.
翻译:许多城市和大型组织都有车辆车队,需要协调处理垃圾收集或基础设施检查等任务。出于这一需要,本文件侧重于共同的次要问题,即车辆小组需要规划协调路线,在迭代地区巡逻,同时尽量减少时间和空间依赖成本。特别是,在一个特定地点(例如图上的一个顶点),我们假设费用线性增长,预期速度未知,每当任何车辆访问顶端(代表为顶端服务的机器人)时费用将重订为零。我们用图表术语来提出这一问题,称它为“以不确定的Reward(TOCPUR)为Orenteerering Production团队(TOCPUR)) 。我们建议通过同时估计图上每个顶点的累计成本,并解决“Orenteerering Group”小组(TOP)的新变式,我们称之为“Oreentegenteeration Conditional Group”小组(TOCP),我们为TOCP提供了第一个混合的编程设计方案,作为原TOP的显著调整。我们建议采用的一种新的图式方法,我们提出了一种由数百个任意的算算出一种新的方法。我们用新的方法来比较一种新的方法。我们用一种新的方法来比较一种方法。