Hanzhao Wu

Papers

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About

Hanzhao Wu is a robotics researcher whose work focuses on the intersection of motion planning, autonomous navigation, and optimization for mobile robots operating in constrained environments. His key contributions lie in developing practical, real-time solutions for logistics robots and autonomous systems, addressing the critical challenge of safe and efficient path planning on narrow roads and in complex settings. His 2022 paper, "An Optimization-Based Motion Planner for Car-like Logistics Robots on Narrow Roads" (8 citations), tackles the difficulty of generating smooth, optimal paths for non-holonomic vehicles in restricted logistics parks, a problem vital for industrial automation. More recently, in 2024, he introduced "Soft Actor-Critic Combining Potential Field for Global Path Planning of Autonomous Mobile Robot" (6 citations), a novel approach that integrates reinforcement learning with artificial potential fields to improve global navigation. This work demonstrates his ability to blend classical robotics methods with modern machine learning techniques. With a growing citation footprint, Wu's research is directly impacting the development of more capable and autonomous logistics and mobile robots, making his work essential reading for students and researchers in field robotics and intelligent transportation.