(Georgia Institute of Technology, 2010-06)
Kim, Jonghoek; Zhang, Fumin; Egerstedt, Magnus B.
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.