Stochastic Image-based Visual Predictive Control

Image-based visual predictive controllers have gained attention due to their optimality and constraint-handling capabilities. However, their performance deteriorates in presence of the modelling and measurement uncertainties. This paper presents a stochastic image-based visual predictive control method to overcome some shortcomings of the previous schemes cited in literature. In particular, the proposed approach provides a systematic solution to address the image-based constraint compliance in presence of the measurement and modelling uncertainties. The proposed method was implemented on a 6-DOF Denso robot via simulation.