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About

Hongliang Yuan is a robotics and autonomous systems researcher whose work centers on mobile robot navigation, motion planning, and reinforcement learning-based control. His research addresses one of the most fundamental challenges in robotics: enabling robots to move safely and efficiently through complex, unpredictable environments. Yuan's most significant contributions lie in model-based motion planning for mobile robots operating in unknown dynamic environments. His 2013 work on collision-free motion planning for nonholonomic robots — his most-cited paper with 9 citations — introduced a real-time approach that incorporates dynamic robot models to generate feasible, closed-form trajectories governed by optimal performance criteria and collision avoidance constraints. This framework was further developed in earlier 2011 research, establishing a foundation for computationally efficient replanning. Alongside motion planning, Yuan has made meaningful contributions to reinforcement learning for robot navigation. His development of error-sensitive and cyclic error correction approaches to Q-learning — earning 8 and 2 citations respectively — introduced novel mechanisms for regulating learning rates and Q-value updates, improving both convergence and navigation reliability. Together, these works reflect a coherent research vision: bridging optimal control theory and adaptive learning to produce robust, intelligent autonomous robots capable of operating in real-world conditions.