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About

Dr. Jiangbo Lan is a robotics researcher whose work focuses on advancing the autonomy, precision, and safety of robotic systems in complex environments. His primary research areas include robot path planning, environmental perception, and industrial robot accuracy compensation. Dr. Lan’s major contributions include developing a path-planning method for wall surface inspection robots using an improved genetic algorithm, which addresses critical positioning failures in GPS-denied environments—a key safety advancement. He has also pioneered a glass recognition and map optimization method for mobile robots, enhancing perceptual accuracy in environments with challenging materials like glass. In industrial robotics, Dr. Lan proposed an off-line error compensation method combining differential evolution and deep belief networks to significantly improve absolute positioning accuracy, a persistent challenge in intelligent manufacturing. His work on dynamic target grasping, using an affine group improved Gaussian resampling particle filter, advances real-time tracking and grasping of moving objects. With over 38 citations across his most-cited papers, Dr. Lan’s research is steadily gaining recognition for its practical impact on inspection, manufacturing, and autonomous navigation.