Implementation-aware design of image-based control with on-line measurable variable-delay

Abstract

Image-based control uses image-processing algorithms to acquire sensing information. The sensing delay associated with the image-processing algorithm is typically platform-dependent and time-varying. Modern embedded platforms allow to characterize the sensing delay at design-time obtaining a delay histogram, and at run-time measuring its precise value. We exploit this knowledge to design variable-delay controllers. This design also takes into account the resource configuration of the image processing algorithm: sequential (with one processing resource) or pipelined (with multiprocessing capabilities). Since the control performance strongly depends on the model quality, we present a simulation benchmark that uses the model uncertainty and the delay histogram to obtain bounds on control performance. Our benchmark is used to select a variable-delay controller and a resource configuration that outperform a constant worst-case delay controller.