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About

Fei Gao is a robotics researcher whose work spans multi-robot coordination, autonomous navigation, trajectory planning, and whole-body motion planning — areas that sit at the exciting intersection of perception, control, and real-time decision-making. His most recognized contribution, the Meeting-Merging-Mission (M3) framework (2022, 60 citations), addresses a critical challenge in real-world multi-robot deployment: coordinating exploration teams under strict communication constraints. By introducing lightweight environment representations and efficient cooperative strategies, this work has become a meaningful reference for large-scale autonomous systems research. Gao has also made notable strides in aerial robotics, developing an adaptive real-time trajectory planner for aerial perching (2022, 36 citations) that removes the need for pre-determined terminal states — a practical advancement for dynamic, unstructured environments. His work on LF-VIO (2022, 18 citations) broadens the applicability of visual-inertial odometry to wide field-of-view cameras, enhancing perception for autonomous vehicles and robots alike. More recently, his research on collision evaluation algorithms and whole-body motion planning for mobile manipulators demonstrates a commitment to making complex robotic systems computationally tractable and deployment-ready. Collectively, Gao's work reflects a strong focus on bridging theoretical rigor with real-world robotic applicability.