Hai-Jun Rong

Papers

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About

Dr. Hai-Jun Rong is a leading researcher in intelligent robotics and autonomous navigation, with a focus on developing novel machine learning algorithms for real-time robot control. His work centers on the intersection of neural networks, reinforcement learning, and motion planning, particularly for enabling mobile robots and autonomous vehicles to operate safely in unknown, dynamic environments. Dr. Rong’s most significant contribution is the introduction of a random neural Q-learning strategy for obstacle avoidance, which allows a mobile robot to learn collision-free paths without prior environmental knowledge—a foundational paper that has garnered 13 citations. He has further advanced the field by integrating Vector Field Histogram (VFH) methods with Bézier curves to address the nonholonomic constraints of autonomous vehicles, enabling precise goal-directed navigation. Additionally, Dr. Rong pioneered the least mean p-power extreme learning machine (LMP-ELM), a robust neural learning algorithm that enhances Q-learning-based obstacle avoidance in uncertain settings. His work is highly regarded for its practical impact on autonomous systems, bridging theoretical machine learning with real-world robotic applications.