Papers
8
Total Citations
311
H-Index
5
About
Yamei Luo is a leading researcher in robotics and neural dynamics, specializing in motion planning and coordination for humanoid and redundant robotic systems. Her work centers on overcoming critical challenges such as joint-angle drift, high joint velocities, and obstacle avoidance in complex environments. Luo’s most influential contribution, the 2015 paper on a neural-dynamic-method-based dual-arm cyclic-motion generation (DACMG) scheme for humanoid robots, has garnered 148 citations, establishing a foundational approach to remedying joint-angle drift. She further advanced the field with a tricriteria optimization-coordination-motion (TCOCM) scheme for dual-redundant-robot manipulators (74 citations), addressing discontinuity and velocity issues in path planning. Her 2020 vector-based constrained obstacle avoidance (VOA) scheme for wheeled mobile redundant robot manipulators (43 citations) introduced novel solutions for safe navigation in cluttered settings. More recently, Luo has pioneered the use of varying-parameter complementary neural networks for multi-robot tracking and formation control, and explored large-model and generative-intelligence systems for agricultural robotics. Her work, which also includes state-coupled neural networks for distributed robotic arms, consistently pushes the boundaries of real-time, adaptive robot control, making her a pivotal figure in advancing autonomous multi-robot collaboration.
Research Focus
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Top Papers
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- 7Large-Model and Generative-Intelligence Agricultural Robot Systems*4 citations · 2023
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