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About
Gan Zhao is a researcher focused on advancing the safety and autonomy of mobile robotic systems, particularly in complex, obstacle-laden environments. His primary research areas include nonlinear model predictive control (NMPC), safety-critical control, and motion planning for car-like robots. Zhao’s major contribution lies in developing a safety-critical NMPC framework that enables robots with limited detection capabilities to reliably track targets while navigating around obstacles. By introducing a temporary artificial reference point within the robot’s detection region, his work effectively mitigates the risk of collisions when the target moves beyond the sensor’s field of view. This approach is especially valuable for real-world applications where sensor constraints are common. His most-cited paper, “Safety Critical NMPC in Obstacle-Existing Scenes for Car-Like Mobile Robots With Limited Detection” (2025), has already garnered attention for its practical relevance and innovative solution to a persistent challenge in mobile robotics. Zhao’s research bridges the gap between theoretical control methods and practical deployment, offering a robust pathway for safer autonomous navigation in dynamic, obstacle-rich settings.
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