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About

Haili Xu is a robotics researcher whose work spans trajectory optimization, mobile robot navigation, multi-robot coordination, and computer vision for industrial applications. Best known for contributions to optimal robot motion planning, Xu developed techniques for globally optimizing time-energy trajectories of industrial robot manipulators — work that accounts for dynamic equations of motion alongside constraints on joint velocities, accelerations, jerks, and torques. This research, published across 2009 and 2010, has garnered a combined 57 citations and remains a meaningful reference in the field of robot trajectory optimization. Beyond industrial manipulators, Xu has explored mobile robotics extensively, proposing an improved hybrid path-planning method that merges potential field approaches with wall-following algorithms to overcome local minimum problems and odometer drift in real-world environments. Earlier work demonstrated innovative applications of artificial immune network theory to multi-robot cooperation, reflecting a broad interdisciplinary curiosity. Xu also contributed to nonholonomic wheeled mobile robot stabilization using potential fields combined with genetic algorithms, as well as 3D pose estimation for vision-equipped industrial robots. Across these diverse directions, Xu's research consistently bridges theoretical foundations with practical robotic implementation, making the body of work valuable for students and engineers working at the intersection of motion planning, autonomous navigation, and industrial automation.