Battery Level Estimation of Mobile Agents under Communication Constraints

Consider a team of mobile agents monitoring large areas, e.g. in the ocean or the atmosphere, with limited sensing resources. Only the leader transmits information to other agents, and the leader has a role to monitor battery levels of all other agents. Every now and then, the leader commands all other agents to move toward or away from the leader with speeds proportional to their battery levels. The leader then simultaneously estimates the battery levels of all other agents from measurements of the relative distances between the leader and other agents. We propose a nonlinear system model that integrates a particle motion model and a dynamic battery model that has demonstrated high accuracy in battery capacity prediction. The extended Kalman filter (EKF) is applied to this nonlinear model to estimate the battery level of each agent. We improve the EKF so that, in addition to gain optimization embedded in the EKF, the motions of agents are controlled to minimize estimation error. Simulation results are presented to demonstrate effectiveness of the proposed method.