Best Student Paper on Robotics, ASME Dynamic Systems and Control Division

My student Hui Xiao received this Best Student Paper on Robotics award on October 11, 2019, from the ASME Dynamic Systems and Control Division. Our paper, titled “Following fast-dynamic targets with only slow and delayed visual feedback - a Kalman filter and model-based prediction approach,” addresses key challenges in real-time vision-based robotic applications.

While visual feedback has revolutionized robotic capabilities such as autonomous navigation and robotic surgery, limitations in sampling rate and time delays remain significant barriers to real-time performance. In this work, we explored the potential for overcoming these challenges when partial knowledge of target dynamics is available. Specifically, we developed a novel framework that integrates Kalman filters with multirate model-based prediction to: 1. Reconstruct high-frequency 3D target position and velocity data, and 2. Compensate for time delays under general robotic motion profiles.

We further analyzed the impact of modeling choices and delay durations, built simulation tools, and validated the proposed algorithms experimentally with a robotic manipulator equipped with an eye-in-hand camera. The results demonstrated that our approach enables robots to track fast-moving targets effectively, even with slow and delayed visual measurements.

My sincere gratitude goes to Hui for his exceptional contributions and to the ASME Dynamic Systems and Control Division for this recognition.