Abstract
Computer vision has been revolutionised in recent years by increased research in convolutional neural networks (CNNs); however, many challenges remain to be addressed in order to ensure fast and accurate image processing when applying these techniques to robotics. These challenges consist of handling extreme changes in scale, illumination, noise, and viewing angles of a moving object. The project main contribution is to provide insight on how to properly train a convolutional neural network (CNN), a specific type of DNN, for object tracking in the context of industrial robotics. The proposed solution aims to use a combination of documented approaches to replicate a pick-and-place task with an industrial robot using computer vision feeding a YOLOv3 CNN. Experimental tests, designed to investigate the requirements of training the CNN in this context, were performed using a variety of objects that differed in shape and size in a controlled environment. The general focus was to detect the objects based on their shape; as a result, a suitable and secure grasp could be selected by the robot. The findings in this article reflect the challenges of training the CNN through brute force. It also highlights the different methods of annotating images and the ensuing results obtained after training the neural network.
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Notes
- 1.
Max pooling is a technique that extracts the most significant features from the convolutional layer.
- 2.
Retrieved from https://www.syntouchinc.com/en/sensor-technology/, last accessed 2019-06-20.
- 3.
Retrieved from https://www.active8robots.com/robots/ar10-robotic-hand/, last accessed 2019-06-20.
- 4.
Retrieved from https://medium.com/@manivannan_data/how-to-train-YOLOv3-to-detect-custom-objects-ccbcafeb13d2, last accessed on 25/04/2019.
- 5.
Available online, https://gitlab.com/CNCR-NTU/CNCR_annotation_tool, last accessed on the 15/06/2019.
- 6.
Available online https://www.youtube.com/watch?v=vdDqMtdyUYU, last accessed on 25/04/2019.
- 7.
Available online https://www.youtube.com/watch?v=IzN3kp7eAuY, last accessed on 25/04/2019.
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Brandenburg, S., Machado, P., Shinde, P., Ferreira, J.F., McGinnity, T.M. (2020). Object Classification for Robotic Platforms. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_17
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