Zhijun Zhao
Papers
3
Total Citations
26
H-Index
3
About
Zhijun Zhao is a rising researcher in space robotics and autonomous systems, with a focus on intelligent path planning and manipulation for extraterrestrial exploration. His key research areas include deep reinforcement learning for lunar rover navigation, transmission error analysis for space robot joints, and motion planning for continuum robots used in active debris removal. Zhao’s major contributions center on integrating artificial potential field methods with advanced learning algorithms to enhance robotic autonomy in challenging space environments. Notably, his 2024 work on lunar rover path planning, which combines heuristic approaches with deep reinforcement learning to enable efficient, obstacle-aware navigation for construction tasks at the International Lunar Research Station, has garnered 18 citations. His 2025 analysis of harmonic reducer transmission errors provides critical insights for improving precision in space robot joints under variable loads, while his 2023 method for pre-grasping motion planning with continuum robots advances debris removal capabilities. Zhao’s research, though early in its citation impact, addresses foundational challenges for upcoming lunar missions and orbital sustainability, positioning him as a contributor to the next generation of autonomous space robotics.
Research Focus
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Top Papers
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