Leping Yang

Shanghai University, National University of Defense Technology

Papers

Total Citations

H-Index

About

Leping Yang is a researcher whose work bridges the fields of agricultural robotics and space engineering, demonstrating a unique versatility in robotic systems design. His primary research areas include computer vision for agricultural automation, flexible end-effector design for harvesting robots, and motion planning for free-floating space robots. Yang’s major contributions are centered on developing practical, efficient solutions for robotic manipulation in challenging environments. His most cited work, "Tomato detection based on convolutional neural network for robotic application" (2022, 10 citations), addresses a critical bottleneck in agricultural automation by enabling accurate fruit detection, directly impacting harvesting efficiency. He further advanced this field with the "Design of a Flexible End-Effector for a Tomato Harvesting Robot" (2023), showcasing his ability to integrate perception with physical manipulation. In a different domain, his earlier work on "Motion planning of Free-Floating Space Robot based on gauss pseudo-spectral method" (2012) tackles the complex problem of nonholonomic motion planning for space robots, aiming to minimize base attitude disturbance. With a total of 14 citations across his top papers, Yang’s research is notable for its practical application in both terrestrial agriculture and extraterrestrial robotics, highlighting his innovative approach to solving real-world robotic challenges.