Scanning the Environment with Two Independent Cameras - Biologically Motivated Approach

In this paper we present a novel method for visual scanning and target tracking by means of independent pan-tilt cameras which mimic the chameleon visual system. We present a systematic and optimization-based approach to the problem, from the high-level to the low-level control. In particular, in the first part we develop a new algorithm for scanning the sphere using multiple cameras. The algorithm combines information about the environment and a model of target movement, to perform optimal scanning by means of stochastic dynamic programming. In the second part we develop a model-based control strategy for target tracking. A switching optimal control strategy based on smooth pursuit and saccades is designed by means of explicit model predictive control (MPC) theory. We simulated and experimentally validated our theory on a robotic chameleon head composed of two independent pan-tilt cameras. The resulting scanning pattern and target tracking has a remarkable resemblance to the one seen in nature by chameleons