Distributed Global 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 a solution that not only achieves the global optimality in the agents configuration but also effectively handles the issue of agents remaining stationary in regions void of information. 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 towards the 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 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.