Chendi Qu

Papers

Total Citations

H-Index

About

Chendi Qu is a researcher focused on advancing autonomous navigation and control systems for mobile robotics, with a particular emphasis on dynamic environment interaction and optimal control theory. Their work bridges the gap between classical control methods and modern learning-based approaches, addressing critical challenges in real-time target interception and controller synthesis. Qu’s most cited paper, “Moving Target Interception Considering Dynamic Environment” (2022, 13 citations), introduces a novel algorithm that combines polynomial fitting for target trajectory prediction with wheeled mobile robot control, enabling effective interception in unpredictable settings—a contribution with direct applications in surveillance, autonomous delivery, and search-and-rescue operations. More recently, in “Control Law Learning Based on LQR Reconstruction With Inverse Optimal Control” (2024), Qu demonstrates a groundbreaking method to recover optimal linear quadratic regulator (LQR) controllers from observed trajectories, effectively allowing robots to learn control laws from demonstration without explicit programming. This work, though newly published, signals a significant step toward more adaptable and intelligent autonomous systems. By integrating predictive modeling with inverse optimal control, Qu is shaping the future of robotics, offering tools that make machines more responsive and efficient in complex, real-world environments.