(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.
(Georgia Institute of Technology, 2010)
Leonard, Naomi E.; Paley, David A.; Davis, Russ E.; Fratantoni, David M.; Lekien, Francois; Zhang, Fumin
A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sampling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of autonomous vehicles carrying environmental sensors to efficiently sample the ocean. We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Six of these gliders were coordinated autonomously for 24 days straight using feedback laws that scale with the number of vehicles. These feedback laws were systematically computed using recently developed methodology to produce desired collective motion patterns, tuned to the spatial and temporal scales in the sampled fields for the purpose of reducing statistical uncertainty in field estimates. The implementation was designed to allow for adaptation of coordinated sampling patterns using human-in-the-loop decision making, guided by optimization and prediction tools. The results demonstrate an innovative tool for ocean sampling and provide a proof of concept for an important field robotics endeavor that integrates coordinated motion control with adaptive sampling.