Event- and deadline-driven control of a self-localizing robot with vision-induced delays

Control based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data leads to significant delays with a large variability in the control loops. This often causes performance deterioration, since in many cases the delay variability is not explicitly addressed in the control design. In this work, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based selflocalization algorithm. The completion time or delay of the RANSAC-based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data-processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index.