Papers
20
Total Citations
257
H-Index
10
About
Kene Li is a robotics and computational intelligence researcher whose work centers on motion planning, kinematic control, and neural network-based optimization for redundant robot manipulators. With a body of research spanning over a decade, Li has made substantial contributions to solving fundamental challenges in robotic systems, particularly the discontinuity problems that arise in velocity and acceleration minimization schemes. Among Li's most influential contributions is the development of bi-criteria minimization frameworks that elegantly balance competing performance objectives in robot motion planning. His pioneering work on Linear Variational Inequalities-based primal-dual neural networks provided a computationally efficient solver for real-time kinematic control, garnering 42 and 28 citations in landmark papers. Li has also advanced fault-tolerant motion planning strategies, ensuring robust manipulator performance under hardware failures — a practically critical achievement cited 42 times. Beyond theoretical contributions, Li's research demonstrates strong experimental grounding, including real-time joystick control experiments and validation using the widely recognized PUMA560 robot platform. His acceleration-level obstacle-avoidance schemes and manipulability-maximizing self-motion planning further reflect a commitment to bridging algorithmic innovation with physical robot systems. Collectively, Li's work has meaningfully shaped how researchers approach optimization, neural computation, and reliable control in modern redundant robotics.
Research Focus
Key Achievements
Top Papers
- 1Fault-tolerant motion planning and control of redundant manipulator42 citations · 2011
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