A decision-theoretic approach to planning, perception, and control

Abstract

The application of Bayesian decision theory as a framework for designing high-level robotic control systems is discussed. The approach to building planning and control systems integrates sensor fusion, prediction, and sequential decision making. The system explicitly uses the value of sensor information as well as the value of actions that facilitate further sensing. A stochastic decision model and a model for mobile-target localization used in the control system are described. A control system implemented to drive a small mobile robot equipped with eight sonar transducers with a maximum range of six meters and a visual processing system capable of identifying moving targets in its visual field and reporting their motion relative to the robot is also discussed. >