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About

Yujun Wu’s research lies at the intersection of robotics, computer vision, and agricultural automation, with a focus on developing intelligent systems that bridge perception and manipulation. Their most cited work introduces a novel serial-parallel hybrid finger mechanism actuated by twisted-and-coiled polymer, a design that advances soft robotics by combining compliant actuation with precise kinematic control. Wu’s contributions to agricultural robotics are exemplified by a tomato harvesting robot system that leverages binocular vision and deep learning for real-time fruit detection and accurate 3D localization, directly addressing labor shortages in farming. In manufacturing, Wu developed an automatic calibration algorithm for robot Tool Center Points (TCP) using binocular vision, enhancing precision in industrial operations. A particularly innovative application appears in biomedical engineering, where Wu proposed a semi-automatic method for 6D pose estimation of rat skulls using 3D vision techniques—a low-cost, real-time alternative to MRI/CT for preclinical brain surgery. With over 20 combined citations across these works, Wu demonstrates a consistent ability to integrate vision-based sensing with robotic actuation, producing practical solutions that reduce human labor and improve accuracy in domains from agriculture to medicine.