A Coverage Algorithm for Multiple Autonomous Surface Vehicles in Flowing Environments

This paper is concerned with the coverage problem with multiple autonomous surface vehicles (ASVs) in time-varying flowing environment, where the interest information distribution is unknown to the coverage networks. While taking the model parameter uncertainty into consideration, a decentralized, adaptive control law is proposed such that the coverage network will converge to the optimal assigned region from arbitrary positions. For ease of exploration, we first investigate the static coverage problem of two-agent systems in flowing environment and present an example by extending the two-agent systems into the general case. In addition, Gaussian Estimation is introduced to predict the value of the sensory function through the sampled measurements. By using the static coverage partition as theoretical foundation, we transform the optimal coverage control into the moving target tracking problems, where the target is the centroid of the assigned region for each ASV. Based on these techniques, a decentralized kinematic control algorithm is developed to navigate the multi-ASV systems. Furthermore, the adaptive back-stepping techniques are employed to extend the kinematic controller into dynamic case with uncertain model parameters. Finally, simulation studies are provided to demonstrate the feasibility and effectiveness of the proposed approaches.