Guoshun Cui

Papers

Total Citations

H-Index

About

Guoshun Cui is a roboticist advancing autonomous manipulation through intelligent motion planning and perception-driven grasping. His research centers on two critical challenges in robotics: efficient obstacle avoidance path planning and adaptive grasping of deformable objects. Cui’s most cited work introduces the **CMN-RRT* algorithm**, a novel motion planning method for robotic arms that enhances path efficiency and obstacle avoidance by leveraging central multi-node sampling. Tested on the KINOVA JACO GEN2 arm, this approach addresses growing industrial demands for faster, safer robotic motion, earning 4 citations since 2023. More recently, Cui developed the **VCFN-YOLOv8 framework**, a deep learning architecture that classifies soft object grasping states—such as under-grasp, over-grasp, and optimal grasp—by analyzing visual cues and grasping angles. This work tackles a fundamental challenge: enabling robots to adaptively grasp soft objects without excessive deformation or slippage, a task humans perform effortlessly but robots find difficult. Though early in its impact (1 citation in 2025), this research promises to bridge the gap between human-like dexterity and robotic precision. Cui’s contributions are shaping the next generation of adaptive, collision-free robotic systems for manufacturing and service applications.