Yisheng Zhang
Papers
2
Total Citations
40
H-Index
2
About
Yisheng Zhang is a leading researcher at the forefront of sustainable robotics, specializing in autonomous systems for electric vehicle (EV) battery disassembly and recycling. His work directly addresses the critical challenge of efficiently processing end-of-life EV batteries to support environmental protection and the circular economy. Zhang’s major contributions lie in developing robust, explainable robotic systems capable of handling the high uncertainty and variability inherent in disassembling complex battery packs. His most-cited paper, "An Accurate Activate Screw Detection Method for Automatic Electric Vehicle Battery Disassembly" (2023, 29 citations), introduces a novel vision-based method for precisely locating and detecting screws, a key bottleneck in automated disassembly. Building on this, his second highly cited work, "Development of an Autonomous, Explainable, Robust Robotic System for Electric Vehicle Battery Disassembly" (2023, 11 citations), pioneers a NeuroSymbolic task and motion planning architecture that enhances both the reliability and transparency of robotic actions. By integrating deep learning with symbolic reasoning, Zhang is not only advancing the practical feasibility of automated battery disassembly but also setting new standards for explainable AI in industrial robotics, making his research highly influential for both academia and the green manufacturing industry.
Research Focus
Key Achievements
Top Papers
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