Bi-level Voronoi strategy for cooperative search and coverage

In this paper, a bi-level Voronoi-based path planning strategy is proposed to address the challenge of cooperative multi-agent search and coverage in uncertain environments. While traditional Voronoi-based coverage control is commonly utilized for optimal path planning, its limitations, such as agents' premature convergence to Voronoi centroids, leading to reduced exploration and lack of incentive to move, can hinder system efficiency. The proposed bi-level strategy provides a framework to overcome such limitations while ensuring a more balanced and adaptive allocation of the environment among agents, thereby enhancing overall performance in terms of environmental mean uncertainty reduction and target detection. This framework utilizes a primary Voronoi diagram based on agent positions for initial spatial partitioning. To enhance exploration efficiency, a secondary Voronoi tessellation is applied, integrating probabilistic information about the target's existence. The bi-level framework enables agents to achieve purposeful coverage by employing an efficient Voronoi partition allocation that integrates both the agents' positions and the probability of target existence. To this end, a novel allocation approach is employed to assign Voronoi neighbors to agents, ensuring that common cells within each agent's region are allocated to the most deserved agent. This mechanism promotes proportional contributions to uncertainty reduction, ensuring that each agent prioritizes areas of higher uncertainty or greater target likelihood. By doing so, agents operate efficiently, effectively reducing environmental uncertainty and improving target detection. Simulation results and comparative analyses validate the proposed strategy, demonstrating its superiority over conventional methods and highlighting its significance in multi-agent cooperative missions.