Papers
4
Total Citations
107
H-Index
4
About
Yingyi Sun is a researcher whose work spans robotics, path planning, optimization, and intelligent systems. A central theme in Sun’s research is the development of hybrid and adaptive algorithms for autonomous navigation and control. Their most cited work, "A hybrid formation path planning based on A* and multi-target improved artificial potential field algorithm in the 2D random environments" (2022, 82 citations), introduces a novel integration of global and local planning methods to achieve efficient and collision-free multi-agent formation control. Sun has also made significant contributions to bipedal locomotion, proposing a superlinearly convergent trust region-sequential quadratic programming approach for optimal gait control via nonlinear model predictive control (2020). In the domain of applied AI, Sun developed a deep learning-aided system for intelligent reimbursement robots (2019), addressing real-world automation challenges in financial workflows. Further advancing optimization theory, Sun proposed a universal noise-suppressing neural algorithm framework for time-varying quadratic programming problems (2022). With a portfolio of work that bridges theoretical optimization, robotics, and practical AI systems, Yingyi Sun demonstrates a clear impact on both algorithmic foundations and their deployment in complex, real-world environments.
Research Focus
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Top Papers
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