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About

Fanghong Guo is a researcher whose work sits at the intersection of robotics, control theory, and artificial intelligence, with a particular focus on enabling intelligent and autonomous navigation for mobile robots. His key research areas include inertial navigation, collision avoidance, and the integration of classical control with modern machine learning. Guo’s major contributions are exemplified by his most-cited work, "Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot" (2023, 22 citations), which addresses the critical challenge of using low-cost sensors for accurate positioning—a problem central to affordable robotics. He has also advanced safe multi-robot navigation through his study of reciprocal collision avoidance for nonholonomic robots (2018, 8 citations), and explored the practical fusion of PID control with reinforcement learning for industrial process control (2021, 4 citations). Additionally, his work on global path planning for fire-fighting robots using an advanced Bi-RRT algorithm (2021) demonstrates a commitment to real-world, safety-critical applications. Through these contributions, Guo is bridging the gap between theoretical algorithms and robust, deployable robotic systems, making him a notable figure in the field of autonomous mobile robotics.