Bao-Chang Chen

Papers

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About

Bao-Chang Chen is a robotics researcher whose work centers on the practical integration of vision systems with industrial automation, particularly in arc welding. His primary contributions lie in the domain of hand-eye calibration, a critical process that enables welding robots to precisely locate and track weld seams using camera-based sensors. By developing algorithms that accurately transform positional data from camera coordinates to robot base coordinates, Chen has addressed a fundamental challenge in automated welding: ensuring that a robot can "see" and react to its workpiece with high precision. His most cited work, "An algorithm of hand-eye calibration for arc welding robot" (2019), has garnered 8 citations, reflecting its relevance to engineers and researchers working on sensor-guided robotic manipulation. This contribution is notable for its focus on real-time, non-contact seam tracking—a technology increasingly vital in modern manufacturing for improving weld quality and process efficiency. Chen’s research bridges the gap between theoretical calibration methods and their deployment in demanding industrial environments, making his work a practical resource for advancing autonomous robotic welding systems.