Senjing Zheng
Papers
3
Total Citations
26
H-Index
3
About
Dr. Senjing Zheng is a researcher at the forefront of applying deep learning and nature-inspired algorithms to industrial robotics and automation. Her primary research focuses on intelligent shape recognition and automatic identification of mechanical parts, bridging computer vision with robotic manipulation. Dr. Zheng’s most impactful work, "Automatic identification of mechanical parts for robotic disassembly using the PointNet deep neural network" (2022, 12 citations), demonstrates a novel approach to enabling robots to autonomously recognize and handle components during disassembly processes. She further advanced this field with "Primitive shape recognition from real-life scenes using the PointNet deep neural network" (2022, 11 citations), which explores how geometric primitives like spheres, boxes, and cylinders can be extracted from complex real-world scenes to guide robotic grasping and manipulation. Additionally, her investigation into "Shape Recognition for Industrial Robot Manipulation with the Bees Algorithm" (2022, 3 citations) showcases her versatility in applying swarm intelligence to robotics. Dr. Zheng’s work is critical for developing more autonomous, flexible manufacturing systems, and her integration of PointNet architectures with industrial tasks represents a significant step toward smarter, more efficient robotic disassembly and assembly lines.
Research Focus
Key Achievements
Top Papers
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