Papers

9

Total Citations

146

H-Index

6

About

Xiao-Shan Gao is a leading researcher in robotics and automation, with a primary focus on trajectory planning and control for robotic manipulators. His most significant contributions lie in developing practical, efficient algorithms for time-optimal path tracking—work that directly addresses the challenge of maximizing robot performance under real-world constraints. Gao pioneered the use of convex optimization to solve minimum-time trajectory problems, enabling robots to follow paths faster while respecting limits on torque, voltage, and jerk. His 2013 paper on smooth minimum time trajectory planning (34 citations) and his 2015 work on convex optimization-based path tracking (31 citations) are foundational, demonstrating how to fully utilize machine capabilities without sacrificing computational efficiency. Beyond trajectory optimization, Gao has explored vision-guided manipulation (2022, 21 citations) and innovative soft robotics, including a magnetic liquid metal droplet robot (2023, 10 citations) capable of high output force in milli-Newton range. His 2007 review "Mathematics Mechanization and Applications after Thirty Years" (28 citations) reflects a broader interest in algorithmic foundations. With over 140 total citations, Gao’s work is essential reading for researchers in robot motion planning, offering both theoretical rigor and practical, implementable solutions.

Research Focus

Key Achievements

6
H-Index
9
Papers
146
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Practical smooth minimum time trajectory planning for path following robotic manipulators
34 citations · 2013
📈 Most Prolific Year: 2013 (1 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beihang University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago