Abstract
The ability to grasp objects is one of the basic functions of modern industrial robots. The emphasis of this paper is placed on the visual perception system, and in particular, on the data processing method leading to grasp point detection. The solution involved the design of a perceptual system in which it was necessary to use a SWIR sensor that can see through plastic bags and thus provide sufficient image information for possible processing by a neural network. The grasping point detection was tested with three convolutional neural network architectures. The method was evaluated by a generalized intersection over union (gIoU). The superior architecture was Attention U-Net, where gIoU reached 0.8522 in the case of the best model.
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References
International Federation of Robotics. Robot race: The world’s top 10 automated countries (2021). https://ifr.org/ifr-press-releases/news/robot-race-the-worlds-top-10-automated-countries. 8 Mar 2021
Kumra, S., Kanan, C.: Robotic grasp detection using deep convolutional neural networks. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 769–776 (2017)
Du, G., Wang, K., Lian, S., Zhao, K.: Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. Artif. Intell. Rev. 54(3), 1677–1734 (2020). https://doi.org/10.1007/s10462-020-09888-5
Phoxi 3D scanners by Photoneo, 2021: https://www.photoneo.com/products/phoxi-scan-l/. Accessed 5 May 2021
Industrial 3D Laser Scanners 2021: https://www.hexagonmi.com/products/3d-laser-scanners/. Accessed 5 May 2021
Holz, D., Ichim, A.E., Tombari, F., Rusu, R.B., Behnke, S.: Registration with the point cloud library: a modular framework for aligning in 3-D. IEEE Robot. Autom. Mag. 22(4), 110–124 (2015)
Time of flight (TOF) camera 2021: https://na.industrial.panasonic.com/products/sensors/sensors-automotive-industrial-applications/lineup/time-flight-tof-camera-module. Accessed 5 May 2021
Intel® RealSense\(^{{\rm TM}}\) Technology, 2021: https://www.intel.com/content/www/us/en/architecture-and-technology/realsense-overview.html. Accessed 5 May 2021
Wang, C., et al.: Feature sensing and robotic grasping of objects with uncertain information: a review. Sensors 20(13), 3707 (2020)
Basler. Basler ace: https://www.baslerweb.com/en/products/cameras/area-scan-cameras/ace/aca2500-14uc/ 2020. 8 Jan 2020
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1530–1538 (2017)
Do, T.-T., Cai, M., Pham, T., Reid, I.: Deep-6Dpose: recovering 6d object pose from a single RGB image (2018)
Keller, M., Baum, G., Schweizer, M., Bürger, F., Gommel, U., Bauernhansl, T.: Optimized robot systems for future aseptic personalized mass production, vol. 72, pp. 303–309 (2018)
Karras, L., Wright, L., Abram, D., Cox, T., Kouns, D., Akers, M.: Sterile prefilled syringes: current issues in manufacturing and control. Pharm. Technol. 24(10), 188–196 (2000)
Sharma, P., Singh, A.: Era of deep neural networks: a review. In: 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017 (2017)
Jia, Q., Cai, J., Cao, Z., Wu, Y., Zhao, X., Yu, J.: Deep learning for object detection and grasping: a survey, pp. 427–432 (2018)
HDR SWIR camera 2021: https://new-imaging-technologies.com/swir-products/widy-swir/. Accessed 6 May 2021
WiDy SWIR 640G-SE, 2021: https://new-imaging-technologies.com/product/widy-swir-640g-s/. Accessed 8 July 2021
ODR80 OverDrive\(^{{\rm TM}}\) EZ Mount Ring Light, 2021: https://smartvisionlights.com/products/odr80/. Accessed 8 July 2021
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11217, pp. 334–349 (2018)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR, arXiv:abs/1505.04597 (2015)
Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas (2018)
Beheshti, N., Johnsson, L.: Squeeze u-net: a memory and energy efficient image segmentation network. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1495–1504 (2020)
Dolezel, P., Stursa, D., Kopecky, D., Jecha, J.: Memory efficient grasping point detection of nontrivial objects. IEEE Access, pp. 1 (2021)
Dogo, E.M., Afolabi, O.J., Nwulu, N.I., Twala, B., Aigbavboa, C.O.: A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), pp. 92–99 (2018)
Acknowledgment
The work was supported from ERDF/ESF “Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)” \((No.\, CZ.02.1.01/0.0/0.0/17\)_049/0008394).
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Stursa, D., Dolezel, P., Zanon, B.B. (2022). Medical Catheters Grasping Point Detection with Quality Control. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_39
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