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WU Tie-jun is a robotics researcher whose work centers on autonomous mobile robot navigation, multi-robot coordination, and robotic grasping. Over the course of the 2000s and early 2010s, Wu made sustained contributions to the field of robot path planning, developing innovative algorithms capable of operating in dynamic, uncertain real-world environments. His early and influential work introduced autoregressive (AR) modeling to predict the future positions of moving obstacles, enabling online real-time path planning that significantly advanced the state of the art in adaptive robot navigation (17 citations). Wu further pioneered cooperative co-evolutionary approaches to multi-robot systems, designing distributed, parallelizable algorithms that allow multiple mobile robots to coordinate collision-free movement without centralized control (13 citations). His 2005 work on real-time path planning, integrating global and local planning strategies using polar coordinates, provided a practical framework widely referenced by subsequent researchers (10 citations). Later, Wu expanded his scope to robotic manipulation, contributing methods for stable grasp evaluation and contact point planning using ray-shooting algorithms and dynamic force distribution analysis. Collectively, Wu's research portfolio represents a coherent and impactful body of work bridging computational intelligence, evolutionary algorithms, and practical robotics applications, providing foundational tools for autonomous systems operating in complex environments.