Papers
2
Total Citations
39
H-Index
2
About
Lingjian Ye is a robotics researcher whose work focuses on enabling autonomous systems to navigate and operate effectively in complex, dynamic environments. His primary research areas include real-time path planning, iterative learning control, and trajectory tracking for robotic manipulators. Ye’s major contributions lie in developing algorithms that allow robots to adapt to uncertainty and obstacles in real-time. Notably, his 2022 paper on "Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment" (21 citations) addresses the critical challenge of navigating narrow corridors with mobile obstacles, proposing a novel approach for on-the-fly path generation. Complementing this, his 2020 work on "Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters" (18 citations) introduces an improved quadratic-criterion-based iterative learning control method to enhance tracking precision despite parameter uncertainties. Together, these highly cited papers demonstrate Ye’s impact on advancing robotic autonomy, offering practical solutions for real-world applications where robots must react swiftly to changing conditions. His work is particularly valuable for researchers and students interested in the intersection of control theory, machine learning, and autonomous navigation.
Research Focus
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Top Papers
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