Qihang Zhou
Papers
1
Total Citations
18
H-Index
1
About
Qihang Zhou is a pioneering researcher at the intersection of space robotics and artificial intelligence, with a primary focus on intelligent control systems for free-floating space robots (FFSRs). His most influential work, "Learning to Control a Free-floating Space Robot using Deep Reinforcement Learning" (2019, 18 citations), addresses a fundamental challenge in orbital robotics: the extreme complexity of modeling and controlling robots that operate without a fixed base. Zhou’s major contribution lies in demonstrating that deep reinforcement learning can enable FFSRs to autonomously capture moving targets without requiring explicit kinematic or dynamic model equations—a breakthrough that bypasses traditional control limitations. By allowing the agent to learn closed-loop control policies directly from interaction, his approach significantly reduces the engineering burden of manual system identification. This work has been cited by researchers advancing autonomous satellite servicing, debris removal, and on-orbit assembly. Zhou’s research represents a critical step toward making space robots more adaptive and self-sufficient, with implications for future deep-space missions where real-time human control is impractical.
Research Focus
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Top Papers
- 1