基于图规划的非凸环境公平持久覆盖
Equitable Persistent Coverage of Non-Convex Environments with Graph-Based Planning
AI总结：
针对多机器人在含非凸障碍的连续环境中的持久覆盖问题，提出基于功率图的公平分布式划分与带覆盖误差权重的图规划方法，可求得最优开放覆盖路径，并经仿真与实验验证。
摘要：
本文解决由一个机器人团队持久覆盖复杂非凸环境的问题。我们考虑环境覆盖质量随时间下降、因此需要不断重访每个点的场景。作为第一步，我们的方案找到一种环境划分，使每个机器人的工作量（按各点重要性加权）相等。这通过使用 power diagram（功率图）并依靠一种关于功率权重、可证明正确的分布式控制律来求得公平划分而实现。与现有其他划分方法相比，我们的方案考虑了具有非凸障碍物的连续环境表述。第二步，每个机器人计算一个汇集类扫描路径并覆盖其整个划分区域的图。在每个规划时刻，图顶点处的覆盖误差被分配为相应边的权重。随后，我们的方案能够根据单位行进距离的覆盖误差，高效找到通过该图的最优开放覆盖路径。文中给出了仿真和实验结果以支持所提方案。
英文摘要：
In this paper we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring to constantly revisit every point. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared to other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
