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About

Baichuan Lou is a robotics researcher advancing the frontiers of autonomous systems through innovative work in motion planning, control, and multi-sensor perception. His primary research areas include nonlinear predictive control for autonomous mobile robots, contact-rich manipulation, and sensor fusion calibration. Lou’s most cited work (19 citations) presents a novel integrated chassis control framework for autonomous mobile robots, combining active fault-tolerant control with regenerative braking to enhance both performance and safety. He has also developed a planning framework for robotic insertion tasks that leverages the hydroelastic contact model, addressing the complex force interactions in contact-rich manipulation without relying on extensive sampling or exploration. In sensor fusion, Lou introduced an automatic spatial calibration method for radar-camera systems using geometric constraints and Doppler-optical flow fusion, eliminating the need for calibration boards or special markers. His contributions demonstrate a systematic approach to making autonomous robots more robust, efficient, and practical for real-world applications, with his work spanning from low-level control to high-level perception and planning.