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About

Yimin Yang is a robotics and intelligent systems researcher whose work spans neural network-based control, multi-robot coordination, and autonomous navigation. With a career stretching from the early 2000s to the present, Yang has made sustained contributions to some of the most challenging problems in modern robotics. Yang's most influential work addresses inverse kinematics for robot manipulators using neural networks, garnering 59 citations and offering novel solutions to longstanding challenges in training data collection within joint subspace frameworks. Complementing this, his research on robust tracking control of nonholonomic systems using recurrent neural networks (34 citations) and extreme learning machine-based predictive control for autonomous mobile robots (16 citations) demonstrates a consistent focus on intelligent, adaptive control strategies. Beyond individual robot control, Yang has contributed meaningfully to multi-robot systems, tackling task decomposition, layered task allocation, and particle swarm optimization-based coordination — reflecting a comprehensive vision of collaborative robotic intelligence. His earlier work on dynamic obstacle avoidance using fuzzy inference and cooperative map building further underscores his long-standing engagement with real-world robotic challenges. More recently, Yang has extended his expertise into computer vision with self-supervised monocular depth estimation. Collectively, his body of work reflects a versatile and enduring impact on intelligent robotics research.