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About

Jifeng Guo is a robotics and autonomous systems researcher whose work spans multi-robot coordination, intelligent path planning, space robotics, and mobile robot perception. His most celebrated contribution, "Learning-Based Multi-Robot Formation Control With Obstacle Avoidance" (2021, 101 citations), demonstrates his expertise in applying machine learning to enable adaptive, collision-free robot formations — a critical capability for real-world deployment. Complementing this, his Deep Q-Network-based path planning method (2018, 75 citations) established an influential framework for mobile robots navigating complex, dense environments, while his improved Artificial Potential Field approach further addresses classical planning limitations such as local minima. Beyond terrestrial robotics, Guo has made notable contributions to space systems research. His work on the Space Solar Power Station's in-orbit assembly mission (2016, 90 citations) and human-robot cooperative truss assembly reflects a sustained interest in extraterrestrial construction challenges. His vibration-based terrain classification work (2019, 43 citations) addresses the demanding requirements of planetary rovers, where sensor reliability is paramount. More recently, his deep multi-agent reinforcement learning approach for resilient space manipulators (2024) signals continued innovation at the frontier of autonomous space systems. Across his portfolio, Guo's research uniquely bridges learning-based intelligence, motion planning, and space engineering.