Papers
2
Total Citations
11
H-Index
2
About
Yunzhe He is a rising researcher in robotics and intelligent control, with a core focus on autonomous navigation and robotic manipulation. He has made significant contributions to path planning and real-time grasping systems for complex environments. His most cited work, "Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method" (2025, 9 citations), introduces a novel hybrid algorithm that combines bio-inspired optimization with reactive control, enabling robots to navigate dynamic obstacles more efficiently than traditional methods. In parallel, his paper "Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections" (2024, 2 citations) addresses the critical challenge of real-time grasping in cluttered settings. By proposing a lightweight deep learning model that fuses RGB and depth data, He enables robots to generate accurate grasp poses for unknown objects without heavy computational overhead. This work balances performance and efficiency, making it suitable for practical deployment. Though early in his career, He’s research demonstrates a clear trajectory toward solving real-world robotic challenges, with his hybrid navigation method already attracting attention for its innovative optimization approach. His work holds promise for advancing autonomous systems in manufacturing, service robotics, and beyond.
Research Focus
Key Achievements
Top Papers
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