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About

Yishen Guo is a robotics and control systems researcher whose work centers on the dynamics and control of space robotic systems, with particular emphasis on free-floating dual-arm space robots. His research tackles one of the field's most challenging problems: developing reliable control strategies for space manipulators operating without fixed bases, where conventional control assumptions break down due to the non-linear parameterization of dynamic equations and the absence of ground reaction forces. Guo's most influential contributions include adaptive neural network control frameworks for coordinating dual-arm motion under uncertain parameters, earning 22 and 8 citations respectively, and adaptive joint-space control methods for managing both base attitude and arm motion simultaneously. His robust control approaches address real-world complications such as external disturbances and multi-object manipulation tasks. Beyond adaptive methods, he has advanced terminal sliding mode control and hierarchical nonholonomic path planning techniques that exploit the inherent mechanical structure of space robots to navigate joint limitations effectively. Collectively accumulating over 60 citations, Guo's body of work provides foundational tools for engineers designing autonomous space manipulation systems — research increasingly relevant as orbital servicing, debris removal, and space assembly missions move from concept to reality.