Papers
13
Total Citations
314
H-Index
9
About
Kenli Li is a leading researcher in robotics, neural computation, and autonomous systems, whose work bridges theoretical advances with real-world robotic applications. His primary research areas include task and motion planning (TAMP) for autonomous robots, zeroing neural networks for solving time-varying equations, and multi-robot coordination. Li’s most impactful contribution is the development of robust finite-time zeroing neural networks (RFTZNNs) for solving dynamic linear equations and Lyapunov equations, with direct applications in robotic tracking and control—his 2018 paper on this topic has garnered 79 citations. He also co-authored a widely cited 2023 survey on TAMP (83 citations), which has become a key reference for researchers working on long-horizon robotic tasks in unstructured environments. Li’s work on quantum evolutionary algorithms for multi-robot coalition formation (22 citations) and GPU-accelerated path planning (AAPP) demonstrates his commitment to scalable, real-time solutions. His research has been published in top venues and is recognized for its depth in both theoretical neural dynamics and practical robotic systems, making him a pivotal figure in advancing intelligent, autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Recent Trends in Task and Motion Planning for Robotics: A Survey83 citations · 2023
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- 5Quantum evolutionary algorithm for multi-robot coalition formation22 citations · 2009
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- 7Point Cloud Acceleration by Exploiting Geometric Similarity14 citations · 2023
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- 10AAPP: An Accelerative and Adaptive Path Planner for Robots on GPU6 citations · 2023