Information-Collection in Robotic Process Monitoring: An Active Perception Approach
This work addresses monitoring and decision-making challenges in manufacturing processes, such as fault diagnosis and safety, but it appears incremental as it applies an existing Bayesian Filter method to a specific domain.
The paper tackles the problem of improving information collection for robotic process monitoring by proposing an active perception approach using a Bayesian Filter, which substantially increases information quality for decision-making in control processes, as demonstrated in physical experiments with a time-varying Kalman Filter for additive manufacturing.
Active perception systems maximizing information gain to support both monitoring and decision making have seen considerable application in recent work. In this paper, we propose and demonstrate a method of acquiring and extrapolating information in an active sensory system through use of a Bayesian Filter. Our approach is motivated by manufacturing processes, where automated visual tracking of system states may aid in fault diagnosis, certification of parts and safety; in extreme cases, our approach may enable novel manufacturing processes relying on monitoring solutions beyond passive perception. We demonstrate how using a Bayesian Filter in active perception scenarios permits reasoning about future actions based on measured as well as unmeasured but propagated state elements, thereby increasing substantially the quality of information available to decision making algorithms used in control of overarching processes. We demonstrate use of our active perception system in physical experiments, where we use a time-varying Kalman Filter to resolve uncertainty for a representative system capturing in additive manufacturing.