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About

Guozheng Lu is a robotics researcher whose work spans motion planning, state estimation, and optimal control, with a particular focus on enabling autonomous robotic systems to operate safely and efficiently in complex real-world environments. His most recognized contribution, "On-Manifold Model Predictive Control for Trajectory Tracking on Robotic Systems" (2022, 46 citations), addresses a fundamental challenge in controlling robots that evolve on geometric manifolds, proposing an elegant MPC framework that avoids the pitfalls of overparameterization and singularities. This builds on his earlier theoretical groundwork in "Model Predictive Control for Trajectory Tracking on Differentiable Manifolds" (2021), establishing a cohesive body of work at the intersection of geometric control theory and practical robotics. His research extends into perception and sensing, demonstrated through his work on IMU-based attitude estimation under narrow-band vibration noise (2019, 23 citations), a critical challenge for agile platforms. More recently, Lu has pushed the boundaries of autonomous flight, with his high-speed MAV navigation framework (2025, 27 citations) showing strong early impact in safety-critical applications such as search and rescue. Collectively, his contributions reflect a rigorous and application-driven approach to advancing autonomous robotic systems.