Coverage Optimization of Robotic Sensor Networks in Nonconvex Environments via Rotary Pointer Partitions

Efficiently handling random events in the uncertain environment requires the farsighted deployment of robotic sensor networks to minimize the service cost. Nevertheless, environmental nonconvexity makes an intangible barrier for spatially distributed design of coverage control algorithms. To solve this dilemma, a novel coverage formulation is proposed for robotic sensor networks with the assistance of rotary pointer partitions. As a result, the coverage region is partitioned into multiple subregions by rotary pointers, and workload equalization can be fulfilled by adjusting phase angles of rotary pointers. Thus, a distributed coverage control algorithm is developed to deploy robotic sensor networks by driving each robot toward the centroid of its own subregion with load balancing. In addition, a simple criterion on communication topology of robotic sensor networks is presented to determine the exponential convergence of partition dynamics. Moreover, a self-perturbation strategy is developed to reduce the service cost without impairing existing workload partition, which improves the coverage quality of robotic sensor networks in a short time. Theoretical analysis is conducted to guarantee the coverage performance of robotic sensor networks, as compared to the classic Voronoi partitions. Finally, numerical simulations are carried out to substantiate the efficacy of the proposed coverage control approach.