Papers
13
Total Citations
449
H-Index
5
About
Chengxi Zhang is a robotics researcher whose work spans path planning, formation control, and calibration for multi-robot systems. His most impactful contribution, an improved artificial potential field method for multi-UAV systems (378 citations), addresses the dual challenges of computing optimal trajectories while maintaining desired formations—a critical problem in aerial robotics. Zhang has also made significant advances in hand-eye calibration, developing globally optimal symbolic solutions that overcome the limitations of existing closed-form and iterative approaches, and tackling the practical issue of mismatched data caused by measurement uncertainties during calibration. His research extends to modular robotic arm design using reinforcement learning, visual-marker-based localization for challenging environments, and fault-tolerant control for underwater vehicles. Zhang’s work on generalized n-dimensional rigid registration provides a theoretical framework with broad applications across robotics and computer vision. His recent contributions include practical prescribed tracking control for robotic manipulators and balance control for unicycle robots under low-power constraints, demonstrating his ability to address both fundamental theory and real-world implementation challenges in robotics and automation systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Globally Optimal Symbolic Hand-Eye Calibration18 citations · 2020
- 3Correspondence Matching and Time Delay Estimation for Hand-eye Calibration16 citations · 2020
- 4
- 5Visual-Marker-Based Localization for Flat-Variation Scene8 citations · 2024
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- 7
- 8Recent Progress in Robot Control Systems: Theory and Applications3 citations · 2023
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