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About

Guofan Wu’s research lies at the intersection of safety-critical control, nonlinear dynamics, and autonomous aerial robotics. His most influential work addresses a fundamental challenge: how to enforce strict safety constraints—such as collision avoidance and limited sensing range—on agile systems like quadrotors. In his highly cited 2016 paper (96 citations), Wu introduced a framework using Control Barrier Functions (CBFs) to guarantee forward invariance of safe sets for planar quadrotors, enabling provably safe operation under dynamic constraints. He extended this to 3D quadrotors with range-limited sensing (37 citations), demonstrating real-time collision avoidance with moving obstacles. Wu also made foundational contributions to nonlinear system linearization, developing a variation-based method for systems evolving on SO(3) and S² manifolds (47 citations), which enables efficient trajectory generation for underactuated robots. His work on partial differential flatness (8 citations) further advanced motion planning for non-flat systems. With over 190 total citations, Wu’s research has shaped modern approaches to safety-critical autonomy, offering rigorous tools that allow robots to operate reliably in complex, uncertain environments.