Papers
9
Total Citations
158
H-Index
6
About
Ning Zhang is a versatile robotics and automation researcher whose work spans robot motion planning, wearable sensing systems, and intelligent process automation. Zhang's most significant contributions lie in advancing the capabilities of six-degree-of-freedom serial robots, with pioneering work on improved artificial potential field methods for obstacle avoidance path planning and BP neural network approaches to inverse kinematics solutions — foundational challenges in industrial robotics that have collectively garnered over 50 citations. Zhang has also made notable strides in human motion analysis, developing a flexible multisensor wearable system combining magnetic-inertial measurement units with flexible sensors to overcome indoor magnetic disturbance limitations. Beyond physical robotics, Zhang has explored the organizational dimensions of automation, producing influential research on robotic process automation (RPA) and business alignment that has shaped how enterprises strategically adopt software automation. Additional contributions include lightweight deep learning models for facial expression recognition, grasping torque optimization for dexterous robotic hands, and an innovative origami-inspired swimming robot design. With nearly 160 cumulative citations, Zhang exemplifies a researcher who bridges theoretical robotics, applied sensing technologies, and real-world automation strategy.
Research Focus
Key Achievements
Top Papers
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- 4Alignment of business in robotic process automation20 citations · 2019
- 5Lightweight Deep Learning Model For Facial Expression Recognition13 citations · 2019
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- 9Decision-Making for RPA-Business Alignment3 citations · 2020