Papers
1
Total Citations
9
H-Index
1
About
Zhan Zuo is a robotics researcher whose work lies at the intersection of space robotics, motion planning, and artificial intelligence. His primary research focuses on developing intelligent control strategies for complex robotic systems, particularly dual-arm space robots operating in challenging environments. Zuo’s most cited paper, “Coordinated Motion Planning of Dual-arm Space Robot with Deep Reinforcement Learning” (2019, 9 citations), introduces a novel approach that combines deep reinforcement learning with the rapidly-exploring random trees (RRT) algorithm to solve coordinated motion planning problems. By establishing kinematic models using the Denavit-Hartenberg method, his work enables dual-arm robots to perform complex, synchronized tasks with greater autonomy and efficiency. This contribution is significant for advancing autonomous operations in space, where real-time decision-making and adaptability are critical. Zuo’s research bridges the gap between traditional robotics and modern machine learning, offering practical solutions for space exploration and industrial automation. His work continues to inspire researchers in robotics and AI, demonstrating the potential of reinforcement learning in real-world robotic applications.
Research Focus
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Top Papers
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