Papers
2
Total Citations
9
H-Index
2
About
Yinggang Li is a researcher advancing the frontiers of intelligent robotics, with a core focus on multi-robot systems (MRSs), adaptive control, and machine learning integration. Li’s major contributions lie in enabling adaptive intelligence for cloud-augmented MRSs, where deep learning techniques are applied to enhance cooperative inference, path planning, and situational awareness in both military and civil applications. This foundational work, published in 2019, has garnered 5 citations and highlights the transformative potential of cloud-based intelligence for robot teams. More recently, Li has addressed the practical challenges of omnidirectional mobile robots, designing an adaptive model predictive controller optimized by an improved genetic algorithm for precise trajectory tracking of Mecanum-wheeled platforms. This 2023 study, with 4 citations, accounts for mechanical and speed constraints, demonstrating Li’s ability to bridge theoretical control methods with real-world robotic constraints. By combining adaptive control with evolutionary optimization, Li’s research offers scalable solutions for autonomous navigation in complex environments. These contributions underscore a commitment to making multi-robot systems more responsive, efficient, and capable—work that is increasingly vital for applications ranging from warehouse automation to search-and-rescue missions.
Research Focus
Key Achievements
Top Papers
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