Papers
8
Total Citations
61
H-Index
4
About
Shuquan Wang is a pioneering researcher in intelligent robotics and space exploration, specializing in reinforcement learning, deep neural networks, and autonomous control systems for extreme environments. His work bridges the gap between theoretical machine learning and practical robotic applications, with a focus on path planning, microgravity operations, and low-gravity locomotion. Wang’s most cited paper, “A Path Planning Approach Based on Q-learning for Robot Arm” (2019, 18 citations), introduced a novel reinforcement learning method for robotic arm navigation, overcoming traditional computational challenges. His 2019 study on deep neural networks for real-time lunar landing control (15 citations) advanced intelligent control for space missions, while his 2022 work on zero gravity robots (10 citations) demonstrated a groundbreaking two-body system for sustained microgravity in China’s Space Station. Wang’s recent contributions include deep reinforcement learning for quadruped robot jumping in lunar environments (2024, 7 citations) and risk assessment models for free-floating space robots (2022, 4 citations). His interdisciplinary approach, spanning agricultural robotics (grape picking) to extraterrestrial exploration, has earned him recognition as a leading figure in space robotics and autonomous systems, with cumulative citations reflecting his growing impact on both terrestrial and off-world robotic technologies.
Research Focus
Key Achievements
Top Papers
- 1A Path Planning Approach Based on Q-learning for Robot Arm18 citations · 2019
- 2Deep Neural Networks Based Real-time Optimal Control for Lunar Landing15 citations · 2019
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