Task Load Based Persistent Coverage Control for Large-Scale Domains Using Multi-Agent Systems with Anisotropic Sensors

Multi-agent coverage control refers to the task of coordinating autonomous robots to effectively survey and gather information across a task-domain. This paper introduces a novel approach that addresses agents equipped with anisotropic sensors and constrained by nonholonomic movement, aiming to ensure persistent coverage and maintain awareness over a large-scale domain. The approach employs redefined Voronoi partitions that leverage each agent's local coverage domain based on its relative task load. The task load herein refers to the level of awareness that each agent has about the domain and the size of its area of dominance, all indirectly encoded in the Voronoi's mass. To account for the vehicles' nonholonomic nature and velocity constraints, the proposed approach is extended using the minimum curvature-based distance when determining the agent's area of dominance. A set of simulations involving teams of three and five agents showcases the effectiveness of the proposed control approach.