Yunfan Gao

Robert Bosch (Germany), University of Freiburg

Papers

Total Citations

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About

Yunfan Gao is a rising researcher in robotics and control systems, with a focused expertise in real-time motion planning and robust model predictive control (MPC) for autonomous systems. His work centers on enabling collision-free navigation for mobile robots and ellipsoidal objects under uncertainty, a critical challenge for self-driving cars and autonomous robotics. Gao’s major contributions include the development of zero-order robust optimization (zoRO) for MPC, which dramatically reduces the computational burden of uncertainty-aware control, making it feasible for real-time applications. His 2023 paper on collision-free motion planning using robust MPC has garnered 11 citations, while his 2024 work on efficient zoRO implementation with the acados framework has already attracted 6 citations, reflecting growing interest in his practical, computationally efficient solutions. Notably, his 2024 paper revisiting collision avoidance for ellipsoidal objects introduces differentiable constraints that ensure non-overlap conditions, offering a fresh geometric approach to a classic problem. Gao’s research bridges the gap between theoretical robustness and real-time feasibility, positioning him as a key contributor to safer, more reliable autonomous navigation.