Distributed estimation and control for robotic sensor networks
In this paper, we address the problem of controlling a network of mobile sensors to estimate a collection of hidden states to a user-specified accuracy. The mobile sensors simultaneously take measurements of the hidden states, fuse them in a distributed filter, and plan their next views in order to minimize expected uncertainty. This formulation leads to an optimization problem with as many coupled LMI constraints as the number of hidden states. The network solves this problem by means of a new distributed random approximate projections method that is robust to the state disagreement errors that will exist among the robots as the distributed filter fuses the collected measurements and is computationally light enough to handle large numbers of hidden states.
