Papers
6
Total Citations
44
H-Index
3
About
Jingkai Cui is a rising expert in robotic manipulation, with a focus on trajectory planning, motion control, and optimization for redundant and dual-arm robotic systems. His work addresses critical challenges in industrial and service robotics, including time-optimal path execution, precise trajectory tracking under uncertainties, and safe interaction with dynamic environments. Cui’s most cited paper (2023, 20 citations) introduces a novel time-optimal asymmetric S-curve trajectory planning algorithm for redundant manipulators, leveraging the whale optimization algorithm to achieve efficient velocity scheduling along specified paths. He further advances control theory with a nonconservative predefined-time sliding mode control scheme (2023, 14 citations) that ensures robust trajectory tracking for uncertain robotic manipulators, offering strong stability guarantees. Cui also develops nature-inspired optimization methods, such as a randomness-enhanced grey wolf optimizer (REGWO) for inverse kinematics and an improved grey wolf optimizer for combined trajectory tracking and obstacle avoidance. His recent work on variable impedance and admittance control for dual-arm systems and contact force tracking (2025, 2022) underscores his commitment to enabling compliant, adaptive robot behavior in real-world tasks. With over 44 citations across his key papers, Cui is establishing himself as a contributor to safer, faster, and more intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6