An LPV approach to synthesizing robust active vision systems

Abstract

Recent hardware developments have rendered controlled active vision a viable option for a broad range of practical problems. However, realizing this potential requires having a framework for synthesizing robust active vision systems, capable of moving beyond carefully controlled environments. It has been shown that this can be achieved by combining robust computer vision and control techniques. However, in some cases robustness is achieved at the expense of performance. In this paper we show that this performance loss can be avoided by recasting the problem into a linear parameter varying (LPV) form and using recently developed robust identification and control tools for this class of problems. These results are experimentally validated using the BiSight robotic head.