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This paper presents an approach to the problem of 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 extend our previous work by showing how modeled system dynamics and configuration space obstacles can be handled. These ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.
computational complexity, cooperative systems, mobile robots, multi-robot systems, optimal control, planning (artificial intelligence), robot vision, sensors, computational resources, computational strategy, configuration space obstacles, dynamic environment, mobile agent team configuration control, modeled system dynamics, particle filtering, sensor control, sensor planning, vision sensors
Date Posted: 18 November 2004
This document has been peer reviewed.