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About

Yanshu Jiang’s research career spans two decades of impactful work in robotics, focusing on the precision, optimization, and intelligent control of robotic systems. Their key research areas include parallel and redundant robot calibration, multi-objective performance optimization, and, more recently, deep learning for robotic grasp detection. Jiang’s major contributions began with pioneering the use of genetic algorithms (GA) for calibrating Stewart parallel robots, achieving significant improvements in positioning accuracy by identifying 24 geometric parameters in a 6-DOF robot—a method that has garnered 17 citations. They advanced the field by introducing the Non-dominated Sorting Differential Evolution (NSDE) algorithm for redundant robots, enabling simultaneous optimization of trajectory tracking, obstacle avoidance, and other performance criteria, cited 7 times. In recent years, Jiang has innovated in robotic grasping with lightweight neural networks like PDCNet, which uses partial convolution and knowledge distillation for efficient grasp detection (5 citations in 2025), and a novel large-kernel residual grasp network (2 citations in 2024). Their work on obstacle avoidance using geometric models and artificial potential fields further demonstrates their versatility. With a total of over 40 citations across six key papers, Jiang’s research bridges classical optimization and modern deep learning, making significant strides in both theoretical foundations and practical applications for autonomous robotic systems.