Zhoulin Chang

Papers

Total Citations

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About

Zhoulin Chang is a robotics and automation researcher whose work focuses on integrating artificial intelligence with mechanical systems for intelligent industrial and environmental applications. Their primary research areas include visual recognition systems, robotic manipulator control, and deep learning-based object detection. Chang’s most influential work, "Design of mobile garbage collection robot based on visual recognition" (2020, 17 citations), introduces an autonomous system capable of path planning, scanning, and picking up recyclable waste using visual recognition—a practical contribution to smart environmental cleanup. In "Research on Manipulator Tracking Control Algorithm Based on RBF Neural Network" (2021, 14 citations), Chang addresses the challenge of controlling highly nonlinear robotic arms by leveraging neural networks for precise trajectory tracking, demonstrating expertise in adaptive control. More recently, "Design of workpiece recognition and sorting system based on deep learning" (2021, 3 citations) applies deep learning to industrial sorting, enabling robots to recognize and categorize objects for automated handling. Together, these works highlight Chang’s commitment to bridging computer vision and robotics, with potential impacts on manufacturing efficiency and autonomous waste management.