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About

Shi Zhong is a leading researcher in space robotics, with a primary focus on the autonomous control and path planning of free-floating space robots. His work addresses the critical challenge of manipulating objects in orbit without fixed base support, where traditional control methods struggle with complex constraints and adaptability. Zhong’s most significant contribution is the development of a novel path planning algorithm based on deep reinforcement learning, specifically the Multi-Agent Deep Deterministic Policy Gradient (MRDDPG) framework, published in 2018 and garnering 14 citations. This work enables space robots to navigate and manipulate targets more effectively by learning optimal trajectories in dynamic, unanchored environments. He has also advanced the field through innovative approaches to minimal dataset construction for capture position recognition, and by proposing hybrid map-based methods in configuration space to minimize kinematic coupling and avoid obstacles—critical for preventing momentum wheel saturation and thruster plume damage during on-orbit operations. With a cumulative citation count exceeding 20 across his key papers, Shi Zhong’s research is foundational for the next generation of autonomous orbital servicing and debris removal missions.