Coverage Control Based on Environmental Learning with Data Sharing among Voronoi Neighbors

This paper proposes an environmental-learning-based coverage control algorithm using a group of multi-rotor UAVs. The proposed method is a decentralized control system where data management and computation are performed by each agent. The environment is learned using Gaussian Process Regression based on data from the agent itself and its Voronoi neighbors. However, as the acquired training data increases, the computational load also increases. Therefore, in this paper, each agent restricts the shared data to those within its own Voronoi region. Additionally, the aim is to shorten operation time by concurrently conducting agent movement and environmental learning. The validity of the proposed method is demonstrated through numerical simulations and experiments.