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About

Tiecheng Su is a leading researcher in teleoperation robotics, specializing in collision risk assessment, tremor suppression, and sensorless force estimation. His work addresses critical challenges in remote robot control, particularly for applications requiring high precision and safety. Su's most cited paper, "Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots" (2022, 26 citations), introduces a framework that enables robots to autonomously navigate hazardous environments while maintaining operator control. He further advanced the field with "Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation" (2021, 24 citations), a novel approach that filters involuntary human hand tremors to improve surgical and industrial precision. Su's innovative "Three-domain Wavelet Least Square Support Vector Machine" (2022, 9 citations) provides an effective tremor-filtering model, while his recent work on "Sensorless force estimation" (2024, 5 citations) and "Graph Robot Network for Force Observer" (2024, 4 citations) eliminates the need for traditional force sensors—a breakthrough for compact or harsh-environment robots. With over 68 cumulative citations, Su's contributions are pivotal for advancing teleoperation safety and dexterity, directly impacting fields like minimally invasive surgery and hazardous material handling.