Guoliang Ye
Papers
2
Total Citations
22
H-Index
2
About
Guoliang Ye is a researcher advancing the frontiers of swarm robotics and multi-manipulator systems, with a focus on intelligent, decentralized coordination under real-world constraints. His work addresses fundamental challenges in collective behavior and motion planning, particularly when communication is limited or workspaces are shared. In his highly cited 2020 study on swarm robotics, Ye introduced a novel target search approach using robot chains with an elimination mechanism, enabling effective cooperation among simple autonomous robots even in restricted communication environments—a critical step toward scalable, resilient swarms. With 11 citations, this work highlights his impact on the field. Simultaneously, Ye tackles the complex problem of collision-free motion planning for dual robotic manipulators, proposing a real-time solution based on recurrent neural networks that accounts for multiple constraints in overlapping workspaces. This research, also garnering 11 citations, demonstrates his ability to bridge theoretical neural network models with practical robotic control. Through these contributions, Ye is shaping the future of autonomous robotic systems, offering efficient, biologically inspired solutions that push the boundaries of what swarms and multi-arm robots can achieve in dynamic, constrained settings.
Research Focus
Key Achievements
Top Papers
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- 2