Distributed optimal coverage control in multi-agent systems: Known and unknown environments

This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents' coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. Theoretical analyses are conducted to assure the asymptotic convergence of agents toward an optimal configuration. A distinguishing feature of this approach lies in its departure from the reliance on geometric methods that are characteristic of Voronoi-based approaches; hence it can be implemented more simply. Remarkably, the technique is adaptive and applicable to various environments with both known and unknown information distributions. Lastly, the efficacy of the proposed method is demonstrated through simulations, and the obtained results are compared with those of Voronoi-based algorithms.