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  4. Vol 7, Issue 3, 2024

Battery Disassembly Exemplification Employing a Vision Sensor for Educational Purposes in Robotics Courses

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DOI: 10.23977/jeeem.2024.070306 | Downloads: 13 | Views: 865

Author(s)

Chunhua Feng 1, Zhuang Liu 1

Affiliation(s)

1 School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Chunhua Feng

ABSTRACT

With the emergence of Industry 5.0, industrial systems are increasingly adopting intelligent manufacturing and human-machine collaboration models. In this context, the recycling of old batteries has become a significant research field. Old batteries contain numerous harmful substances that, if not properly handled, can cause severe environmental pollution. Therefore, the development of efficient and environmentally friendly technology for recycling old batteries has become one of the current focal points of research. The utilization of vision sensors plays a crucial role in automated production lines designed for dismantling old batteries. By employing RGB and depth vision sensors, we can acquire more comprehensive information about target states and utilize advanced image processing algorithms to achieve precise positioning of battery components and guide robots in performing disassembly tasks. 

KEYWORDS

Smart Manufacturing, Human–robot cooperation, Industry 5.0, visual sensor

CITE THIS PAPER

Chunhua Feng, Zhuang Liu, Battery Disassembly Exemplification Employing a Vision Sensor for Educational Purposes in Robotics Courses. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 47-52. DOI: http://dx.doi.org/10.23977/jeeem.2024.070306.

REFERENCES

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[2] Y. Zhang, H. Zhang, Z. Wang, S. Zhang, H. Li, M. Chen, Development of an Autonomous, Explainable, Robust Robotic System for Electric Vehicle Battery Disassembly, in: 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Seattle, WA, USA, 2023: pp. 409–414. https://doi.org/10.1109/AIM46323.2023.10196256.
[3] C.-Y. Wang, A. Bochkovskiy, H.-Y.M. Liao, YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Vancouver, BC, Canada, 2023: pp. 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721.
[4] Q. Zhang, J. Zhu, X. Sun, M. Liu, HTC-Grasp: A Hybrid Transformer-CNN Architecture for Robotic Grasp Detection, Electronics 12 (2023) 1505. https://doi.org/10.3390/electronics12061505.
[5] L. Zhang, X. Zhou, J. Liu, C. Wang, X. Wu, Instance-level 6D pose estimation based on multi-task parameter sharing for robotic grasping, Sci Rep 14 (2024) 7801. https://doi.org/10.1038/s41598-024-58590-x. 

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