Abstract
For the problem of low efficiency of industrial sorting robots that use traditional visual algorithms to identify and locate targets in complex environments. Our system introduces deep learning technology to detect and locate solid waste based on the existing algorithm. In this paper, the industrial robot sorting system platform is built by deep learning technology. Firstly, the visual area on the conveyor belt is captured by the depth camera. The computer uses a trained SSD model to recognize and locate the target, and obtain the information on the type and location of the solid waste. Then, based on the object detection, solid waste objects are segmented by three-dimensional background removal. Finally, the information of the geometric center coordinates and the angle of the long side of the target object are sent to the robot to complete the classification and grabbing of the solid waste. Simulation experiments show that the features learned by using SSD deep neural network have strong robustness and stability in complex environment, and can achieve the solid waste sorting efficiently.
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Acknowledgments
This work was supported National Natural Science Foundation of China (61876167 and U1509207).
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Zhang, Z., Wang, H., Song, H., Zhang, S., Zhang, J. (2019). Industrial Robot Sorting System for Municipal Solid Waste. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11741. Springer, Cham. https://doi.org/10.1007/978-3-030-27532-7_31
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DOI: https://doi.org/10.1007/978-3-030-27532-7_31
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