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About

Bor-Jiunn Wen is a leading researcher in intelligent robotics and automation, specializing in vision-based control systems for agricultural and industrial applications. His work centers on developing autonomous robotic systems that leverage advanced sensing and deep learning to perform complex tasks with high precision. Wen’s major contributions include pioneering automatic fruit harvesting devices using visual feedback control, which addresses labor shortages in agriculture by enabling robots to identify and pick high-quality produce. He also advanced dynamic grip control for robot arms through two-dimensional vision sensing, allowing robots to track and grasp moving targets on conveyor belts—a breakthrough for manufacturing efficiency. His research on dual robotic arms with mutual visual tracking and positioning has further automated assembly processes, simulating screw and nut assembly in real-time. Additionally, Wen has innovated night-time measurement and skeleton recognition using UAVs equipped with LiDAR and deep-learning algorithms, overcoming low-light limitations for environmental monitoring. With over 27 citations across his most-cited papers, Wen’s work has significant impact, notably his 2022 fruit harvesting study (8 citations) and 2018 grip control paper (7 citations). His achievements demonstrate a commitment to practical, scalable automation solutions that enhance productivity and safety in both agriculture and industry.