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About

Pu Shi is a robotics researcher whose work centers on mobile robot navigation, motion planning, and manipulator kinematics. His most significant contributions lie in developing intelligent path planning algorithms that enable robots to navigate safely and efficiently in complex, unknown environments. Shi has made notable advances in artificial potential field (APF) methods, addressing the longstanding local minima problem that traditionally prevented robots from reaching their targets — work that has garnered over 30 citations across multiple studies. His 2010 paper introducing a genetic algorithm-based dynamic path planning scheme, his most cited work with 48 citations, demonstrated how evolutionary computation techniques can be effectively applied to real-time obstacle avoidance. Beyond navigation, Shi has contributed to humanoid robotics through detailed kinematic analysis of 6-DOF manipulators using Denavit-Hartenberg methods, and to autonomous mobile robot power management through applying Unscented Kalman Filter techniques for accurate battery state-of-charge estimation. With a body of work spanning sensor fusion, computational intelligence, and robot kinematics, Shi's research collectively advances the reliability and autonomy of mobile robotic systems operating in real-world, unstructured environments.