Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Dynamic Programming
Learning and Sequential Decision Making
Learning and Sequential Decision Making
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Our work deals with robot multisensor perception and perception resources management. We first describe the stochastic model of a n close-field sensors system gathering information about its environment, and explain how Bayesian formalism applies to such a surveillance automaton. The perception control problem is here the dynamic allocation of these sensors to the different sectors of the horizon, in order to optimize the global estimation of the state. The policy that we propose was tested on a real multisensor robot with an original hardware and software architecture. We try and demonstrate the usefulness of this approach to surveillance and target detection, so that it will eventually become part of a complex system performing various perception requests in an indoor environment.