Application of Deep Learning Methods for Trajectory Planning Based on Image Information

• Autorzy: Babiarz Artur , Kustra Małgorzata , Wen Shuhuan

Identyfikatory

DOI

10.12913/22998624/191924

• Języki publikacji: EN

Abstrakty

EN

This work aims to develop a mobile robot utilizing neural network technology. The algorithm, programmed in Python on a Raspberry Pi 4B platform, is detailed across four main chapters. These chapters cover the fundamental assumptions of deep learning, the construction of the platform, and the research validating pattern recognition accuracy under various disturbances. The mobile platform employs a neural network to analyze selected traffic signs and translates the recognized patterns into corresponding motor movements.

Słowa kluczowe

EN

mobile robot; deep learning; image; neural network

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 6
• Strony: 247--258
• Opis fizyczny: Bibliogr. 19 poz., fig., tab.

Twórcy

autor

Babiarz Artur

• Department of Automatic Control and Robotics, Silesian University of Technology, Akademicka 16 St., 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-7841-7151

autor

Kustra Małgorzata

• Department of Automatic Control and Robotics, Silesian University of Technology, Akademicka 16 St., 44-100 Gliwice, Poland

autor

Wen Shuhuan

• Department of Engineering Research Center Ministry of Education for Intelligent Control System and Intelligent Equipment and the Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China

Bibliografia

• 1. Siciliano, B., Khatib, O., Eds. Springer Handbook of Robotics; Springer, 2016.
• 2. Rybczak, M., Popowniak, N., Lazarowska, A. A survey of machine learning approaches for mobile robot control. Robotics 2024, 13.
• 3. Lee, M.F.R., Yusuf, S.H. Mobile robot navigation using deep reinforcement learning. Processes 2022, 10.
• 4. Bui, T.L., Tran, N.T. Navigation strategy for mobile robot based on computer vision and YOLOV5 network in the unknown environment. Applied Computer Science 2023, 19, 82–95.
• 5. Maturana, D., Scherer, S. 3D Convolutional Neural Networks for Landing Zone Detection from LiDAR. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2015, 3471–3478.
• 6. Nair, V., Hinton, G.E. 3D object recognition with deep belief nets. Advances in neural information processing systems 2009, 22.
• 7. Hinton, G.E., Osindero, S., Teh, Y.W. A fast learning algorithm for deep belief nets. Neural Computation 2006, 18, 1527–1554.
• 8. Shao, J., Kang, K., Loy, C.C., Wang, X. Deeply Learned Attributes for Crowded Scene Understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, 4657–4666.
• 9. Tai, L., Li, S., Liu, M. A Deep-Network Solution Towards Model-Less Obstacle Avoidance. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 2759–2764.
• 10. Li, H., Zhao, R., Wang, X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification. arXiv preprint arXiv:1412.4526 2014.
• 11. Deng, L. The MNIST Database of Handwritten Digit Images for Machine Learning Research. IEEE Signal Processing Magazine 2012, 29, 141–142.
• 12. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y. Reading digits in natural images with unsupervised feature learning. In: Proceedings of the NIPS workshop on deep learning and unsupervised feature learning. Granada, Spain, 2011, 7.
• 13. Coates, A., Ng, A., Lee, H. An Analysis of SingleLayer Networks in Unsupervised Feature Learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence And Statistics. JMLR Workshop and Conference Proceedings, 2011, 215–223.
• 14. Tai, L., Li, S., Liu, M. Autonomous exploration of mobile robots through deep neural networks. International Journal of Advanced Robotic Systems 2017, 14.
• 15. Tai, L., Liu, M. Mobile robots exploration through cnn-based reinforcement learning. Robotics and Biomimetics 2016, 3, 24.
• 16. Sleaman, W.K., Hameed, A.A., Jamil, A. Monocular vision with deep neural networks for autonomous mobile robots navigation. Optik, 2023, 272, 170162.
• 17. Zhang, L., Zhang, Y., Li, Y. Path planning for indoor Mobile robot based on deep learning. Optik 2020, 219, 165096.
• 18. Singh, R., Ren, J., Lin, X. A review of deep reinforcement learning algorithms for mobile robot path planning. Vehicles, 2023, 494(5), 1423–1451.
• 19. Chollet, F. Deep learning with Python; Simon and Schuster, 2021.