Dynamic Sensor Planning and Control for Optimally Tracking Targets

Abstract

In this paper, we present an approach to the problem of actively controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors, since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem, and a practical computational strategy inspired by work on particle filtering for implementing the approach. We then extend our framework by showing how modeled system dynamics and configuration space obstacles can be handled. These ideas have been applied to a target tracking task, and demonstrated both in simulation and with actual robot platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.