LAUSR

This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has communication, sensing, moving, and computation capabilities to complete sensing tasks. These agents would have some limitations on time and energy to accomplish their tasks that need to be considered by the designers. Here, the agents need to relocate themselves, from their initial random locations, to their optimal configuration. An algorithm based on game theory is proposed, where a collection of distributed agents communicate with local neighbors and use their local information make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade-off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Gaussian Mixture Model (GMM) is used to model the distributions of the worth in the sensing area. To estimate the unknown parameters of the GMM, a Maximum Likelihood (ML) estimation scheme is employed, where an expectation-maximization algorithm is used as a tool to solve the ML recursively. Then, in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents’ utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Both simulation results and experimental results on a multi-robot platform are provided to validate the performance of the proposed algorithm.