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About

Fenglin Pang is a leading researcher in robotics and intelligent control systems, with a focus on solving fundamental challenges in robot kinematics, positioning, and motion control. His work centers on developing advanced algorithms that enhance the precision and reliability of robotic systems, particularly in complex indoor environments. Pang’s most influential contribution is his innovative approach to robot inverse kinematics, where he introduced a Fruit Fly Optimization Algorithm (FOA) combined with BP neural networks, a method that significantly outperforms conventional numerical and geometric solutions in both speed and accuracy—a paper that has garnered 59 citations. He further advanced the field with an adaptive federated Kalman filter for indoor mobile robot positioning, addressing the critical issue of single-sensor inaccuracy, cited 50 times. His novel calibration method using an improved Manta Ray Foraging Optimization algorithm for kinematic parameters has also been widely recognized, with 35 citations, demonstrating his ability to reduce absolute positioning errors in manipulators. Additionally, Pang’s work on model predictive control integrated with adaptive neural-fuzzy inference systems for omni-directional service robots showcases his expertise in adaptive control, achieving 19 citations. Through these contributions, Pang has established himself as a key figure in enhancing robotic autonomy and precision, with a growing impact on both academic research and practical robotics applications.