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About

Qinjun Zhao is a robotics researcher whose work focuses on solving fundamental challenges in robot manipulation, navigation, and multi-robot coordination. His primary research areas include inverse kinematics for serial manipulators, sensor fusion for indoor robot localization, and topological mapping for autonomous navigation. Zhao's most impactful contribution is his 2016 paper on using RBF neural networks to solve the inverse kinematics of 6R serial manipulators (11 citations), which addressed the highly complex nonlinear mapping problem that traditional algorithms struggle with. He also developed a novel integrated navigation model coupling IMU, UWB, and encoder data for indoor robots (6 citations), enabling more accurate position estimation. His work on topological map building using thinning algorithms and leader-follower methods for multi-robot cooperation demonstrates his breadth in both single-robot and multi-robot systems. More recently, Zhao has applied his optimization expertise to collaborative path planning for flood control material storage (2022), showing the practical impact of his research. With a career spanning from foundational robotics algorithms to applied disaster response solutions, Zhao's work continues to influence autonomous systems development.