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About

Xuanchen Zhang is a leading researcher in robotics for extreme environments, with a primary focus on the calibration, control, and safety of robotic systems for nuclear fusion reactor maintenance. His most influential work, "Stereo vision based autonomous robot calibration" (102 citations), established a foundational method for achieving high-precision robot positioning without external measurement devices, a critical capability for remote handling in hazardous fusion facilities. Zhang’s contributions extend to advanced kinematic calibration, where he developed a POE-based method using left-invariant error representation for serial robots, and to deformation modeling of heavy-load manipulators using recurrent neural networks. His research also addresses the critical challenge of force perception, with notable work on estimating offsets and gravity parameters for noncontact force compensation with wrist-mounted sensors, and on hybrid collision detection for safe human-robot collaboration. More recently, Zhang has pioneered torque estimation models using BP neural networks to enhance collaborative robot safety. By integrating vision, calibration, force control, and collision detection, his work directly enables the autonomous, precise, and safe operation of robots in the demanding conditions of fusion energy facilities, with his citation record reflecting the growing importance of this field.