Equitable persistent coverage of non-convex environments with graph-based planning

Abstract

In this article, 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 every point to be constantly revisited. 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 with 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.

Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

“…Various techniques for area coverage exist, including Voronoi-based [12], graph-based [13], next best view [14], frontier-based [15], and spanning tree coverage methods [16]. While some techniques provide incomplete coverage, others like spanning tree coverage guarantee complete coverage by generating a non-overlapping path along the spanning tree.…”

Multi-Robot Routing for Persistent Monitoring with Latency Constraints

“…The coverage levels of locations decay with time and robots must keep visiting the locations to maintain a desired coverage level. This scenario of coverage quality deteriorating with time is also considered in [18] where equitable partitions of a continuous non-convex environment are found, and then paths covering the partition for each robot are computed. In [19], the coverage of a location increases depending on the time a robot stays at that location, and the task is to find optimal staying times for robots.…”

Distributed control of mobile robots in an environment with static obstacles

“…The authors in [17,19] use the limited geodesic diagram based on the positions of the robots, to build a distributed control law that maximises the total assigned area. On the other hand, the authors in [20] use static points to generate the geodesic power diagram of the whole region and modify the weights depending on the capabilities of the robots to make the partitions equitable.…”