Volume 22 Issue 1 - Publication Date: 1 January 2003
Dynamic Sensor Planning and Control for Optimally Tracking Targets
John R. Spletzer and Camillo J. Taylor GRASP Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA
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.
Multimedia Key
= Video = Data = Code = Image
Simulation of two robots optimally tracking an unpredictable point target (1.2 MB)
Simulation of four robots optimally tracking three unpredictable point targets (1.5 MB)
Simulation of three ground observers using a dynamical model to optimally track an aerial target (0.8 MB)
Simulation of tracking a point target in a cluttered workspace (1.6 MB)
Experimental trial with two pursuer robots tracking a third target robot in an obstacle-free workspace (0.8 MB)
Experimental trial with two pursuer robots tracking a third target robot in a cluttered workspace Dordrecht. (0.6 MB)
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