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About

Xinying Xu is a pioneering researcher in intelligent robotics, whose work bridges path planning, multi-robot coordination, and sensory perception. Her most influential contribution, "Path Planning and Obstacle-Avoidance for Soccer Robot Based on Artificial Potential Field and Genetic Algorithm" (2006, 21 citations), tackles a classic robotics challenge by fusing the Artificial Potential Field method with Genetic Algorithms to escape local minima—a breakthrough that has guided subsequent work in dynamic environments. She further advanced multi-robot systems with her "Immune ant colony optimization network algorithm for multi-robot path planning" (2014, 11 citations), introducing a bio-inspired hybrid that optimizes cooperative navigation. Xu’s research also extends to haptic and auditory perception, as seen in her work on hierarchical extreme learning machines for tactile recognition (2017) and active auditory exploration for object identification (2020). These studies demonstrate her commitment to equipping robots with human-like senses for real-world interaction. With a career spanning foundational algorithms to cutting-edge sensory intelligence, Xu’s work has shaped how robots perceive, plan, and act—making her a key figure in the evolution of autonomous systems.