Generalization of reference filtering control strategy for 2D/3D visual feedback control of industrial robot manipulators

This paper develops the application of the Dual Rate Dual Sampling Reference Filtering Control Strategy to 2D and 3D visual feedback control. This strategy allows to overcome the problem of sensor latency and to address the problem of control task failure due to visual features leaving the camera field of view. In particular, a Dual Rate Kalman Filter is used to generate inter-sample estimations of the visual features to deal with the problem of vision sensor latency, whereas a Dual Rate Extended Kalman Filter Smoother is used to generate more convenient visual features trajectories in the image plane. Both 2D and 3D visual feedback control approaches are widely analyzed throughout the paper, as well as the overall system performance using different visual feedback controllers, providing a set of results that highlight t he improvements in terms of solution reachability, robustness, and time domain response. The proposed control strategy has been validated on an industrial system with hard real-time limitations, consisting of a 6 DOF industrial manipulator, a 5 MP camera, and a PLC as controller.

Automated summary

This paper presents the application of the Dual Rate Dual Sampling Reference Filtering Control Strategy to 2D and 3D visual feedback control, effectively addressing sensor latency and control task failure caused by visual features leaving the camera field of view. By utilizing a Dual Rate Kalman Filter and Extended Kalman Filter Smoother, improvement in solution reachability, robustness, and time domain response of the system is achieved. The proposed control strategy is validated on an industrial system with real-time constraints, demonstrating its effectiveness in enhancing visual feedback control performance.