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About

Sikang Liu is a robotics researcher whose work sits at the intersection of autonomous flight, motion planning, and 3D mapping for Micro Aerial Vehicles (MAVs). His most celebrated contribution, "Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments" (2017, 537 citations), established a landmark framework for convex-optimization-based trajectory generation that has become foundational in the aerial robotics community. Liu's research consistently tackles the challenge of enabling quadrotors to navigate rapidly and reliably in GPS-denied, obstacle-cluttered environments with limited onboard sensing — a notoriously difficult problem demanding tight integration of perception, planning, and control. His search-based motion planning algorithms, including methods operating in SE(3) for aggressive flight, demonstrate how quadrotors can exploit their full maneuverability to generate smooth, minimum-time trajectories in real time. Beyond navigation, Liu has made significant contributions to autonomous 3D mapping through information-theoretic planning using Cauchy-Schwarz Quadratic Mutual Information (321 combined citations across two works). His later work on trajectory optimization over Riemannian manifolds reflects a broadening theoretical depth. Collectively, his publications have accumulated over 1,600 citations, underscoring his substantial and enduring influence on autonomous aerial robotics.