Robotic target following with slow and delayed visual feedback

摘要

Following rapidly and precisely a moving target has become the core functionality in robotic systems for transportation, manufacturing, and medical devices. Among existing targeting following methods, vision-based tracking continues to thrive as one of the most popular, and is the closest method to human perception. However, the low sampling rate and the time delays of visual outputs fundamentally hinder real-time applications. In this paper, we show the potential of significant performance gain in vision-based target following when partial knowledge of the target dynamics is available. Specifically, we propose a new framework with Kalman filters and multi-rate model-based prediction (1) to reconstruct fast-sampled 3D target position and velocity data, and (2) to compensate the time delay. Along the path, we study the impact of slow sampling and the delay duration, and we experimentally verify different algorithms with a robot manipulator equipped with an eye-in-hand camera. The results show that our approach can achieve 95% error reduction rate compared with the commonly used visual servoing technique, when the target is moving with high speed and the visual measurements are slow and incapable of providing timely information.