Abstract
This paper presents a framework for visual scanning and target tracking with a set of independent pan-tilt cameras. The approach is systematic and based on Model Predictive Control (MPC), and was inspired by our understanding of the chameleon visual system. We make use of the most advanced results in the MPC theory in order to design the scanning and tracking controllers. The scanning algorithm combines information about the environment and a model for the motion of the target to perform optimal scanning based on stochastic MPC. The target tracking controller is a switched control combining smooth pursuit and saccades. Min-Max and minimum-time MPC theory is used for the design of the tracking control laws. We make use of the observed chameleon's behavior to guide the scanning and the tracking controller design procedures, the way they are combined together and their tuning. Finally, simulative and experimental validation of the approach on a robotic chameleon head composed of two independent Pan-Tilt cameras is presented.
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