NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT
Thanh-Lam BUI
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, (Viet Nam)
Ngoc-Tien TRAN
tientn@haui.edu.vnHanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, (Viet Nam)
https://orcid.org/0000-0001-5099-3758
Abstract
Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation
Keywords:
mobile robot, navigation, deep learning, computer visionReferences
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Wang, H., Zhang, F., & Wang, L. (2020). Fruit classification model based on improved Darknet53 convolutional neural network. In 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 881-884). https://doi.org/10.1109/ICITBS49701.2020.00194.
Wang, X., Mizukami, Y., Tada, M., & Matsuno, F. (2021). Navigation of a mobile robot in a dynamic environment using a point cloud map. Artificial Life and Robotics, 26, 10-20. https://doi.org/10.1007/s10015-020-00617-3.
Zhou, L., Rao, X., Li, Y., Zuo, X., Qiao, B., & Lin, Y. (2022). A lightweight object detection method in aerial images based on dense feature fusion path aggregation network. ISPRS International Journal of Geo-Information, 11(3), 189. https://doi.org/10.3390/ijgi11030189.
Al Khatib, E. I., Jaradat, M. A. K., & Abdel-Hafez, M. F. (2020). Low-cost reduced navigation system for mobile robot in indoor/outdoor environments. IEEE Access, 8, 25014-25026. https://doi.org/10.1109/ACCESS.2020.2971169.
Altuntaş, N., Imal, E., Emanet, N., & Öztürk, C. N. (2016). Reinforcement learning-based mobile robot navigation. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3), 1747-1767. https://doi.org/10.3906/elk-1311-129.
Amari, S. I. (1993). Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4-5), 185-196.
Bui, T. L., Nguyen, T. H., & Nguyen, X. T. (2023). A Controller for Delta Parallel Robot Based on Hedge Algebras Method. Journal of Robotics, 2023. https://doi.org/10.1016/0925-2312(93)90006-O.
Chen, C. H., Lin, C. J., Jeng, S. Y., Lin, H. Y., & Yu, C. Y. (2021). Using ultrasonic sensors and a knowledge-based neural fuzzy controller for mobile robot navigation control. Electronics, 10(4), 466. https://doi.org/10.3390/electronics10040466.
Cho, J. H., Song, W., Choi, H., & Kim, T. (2017). Hole filling method for depth image based rendering based on boundary decision. IEEE Signal Processing Letters, 24(3), 329-333. https://doi.org/10.1109/LSP.2017.2661319.
Elfwing, S., Uchibe, E., & Doya, K. (2018). Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107, 3-11. https://doi.org/10.1016/j.neunet.2017.12.012.
Geng, H., Liu, H., Wang, B., & Sun, F. (2018). Reinforcement extreme learning machine for mobile robot navigation. In Proceedings of ELM-2016 (pp. 61-73). https://doi.org/10.1007/978-3-319-57421-9_6.
Gharajeh, M. S., & Jond, H. B. (2020). Hybrid global positioning system-adaptive neuro-fuzzy inference system based autonomous mobile robot navigation. Robotics and Autonomous Systems, 134, 103669. https://doi.org/10.1016/j.robot.2020.103669.
Hichri, B., Gallala, A., Giovannini, F., & Kedziora, S. (2022). Mobile robots path planning and mobile multirobots control: A review. Robotica, 1-14. https://doi.org/10.1017/S0263574722000893.
Hu, M., Wei, Y., Li, M., Yao, H., Deng, W., Tong, M., & Liu, Q. (2022). Bimodal learning engagement recognition from videos in the classroom. Sensors, 22(16), 5932. https://doi.org/10.3390/s22165932.
Iqbal, J., Xu, R., Sun, S., & Li, C. (2020). Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation. Robotics, 9(2), 46. https://doi.org/10.3390/robotics9020046.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
https://doi.org/10.48550/arXiv.1412.6980.
Lagaza, K. P., Kashyap, A. K., & Pandey, A. (2020). Spider monkey optimization algorithm based collision-free navigation and path optimization for a mobile robot in the static environment. In Advances in Mechanical Engineering: Select Proceedings of ICRIDME 2018 (pp. 1459-1473). https://doi.org/10.1007/978-981-15-0124-1_128.
Lin, H., & Yang, J. (2022). Ensemble cross‐stage partial attention network for image classification. IET Image Processing, 16(1), 102-112. https://doi.org/10.1049/ipr2.12335.
Liu, B., Xiao, X., & Stone, P. (2021). A lifelong learning approach to mobile robot navigation. IEEE Robotics and Automation Letters, 6(2), 1090-1096. https://doi.org/10.1109/LRA.2021.3056373.
Liu, Y., Gao, J., Liu, D., & Wang, Z. (2010, October). The design of control system and study on control algorithm of laser navigation mobile robot. In 2010 3rd International Congress on Image and Signal Processing (Vol. 9, pp. 4276-4280). https://doi.org/10.1109/CISP.2010.5647424
Liu, Y., Li, Z., Zhang, T., & Zhao, S. (2018). Brain–robot interface-based navigation control of a mobile robot in corridor environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(8), 3047-3058. https://doi.org/10.1109/TSMC.2018.2833857.
Lulio, L. C., Tronco, M. L., & Porto, A. J. (2009, December). JSEG-based image segmentation in computer vision for agricultural mobile robot navigation. In 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation-(CIRA) (pp. 240-245). https://doi.org/10.1109/CIRA.2009.5423201.
Luo, S., Yu, J., Xi, Y., & Liao, X. (2022). Aircraft target detection in remote sensing images based on improved YOLOv5. IEEE Access, 10, 5184-5192. https://doi.org/10.1109/ACCESS.2022.3140876.
Mourgias-Alexandris, G., Tsakyridis, A., Passalis, N., Tefas, A., Vyrsokinos, K., & Pleros, N. (2019). An all-optical neuron with sigmoid activation function. Optics Express, 27(7), 9620-9630. https://doi.org/10.1364/OE.27.009620.
Nguyen, A. T., & Vu, C. T. (2022). Obstacle Avoidance for Autonomous Mobile Robots Based on Mapping Method. In Proceedings of the International Conference on Advanced Mechanical Engineering, Automation, and Sustainable Development 2021 (AMAS2021) (pp. 810-816). https://doi.org/10.1007/978-3-030-99666-6_118.
Ran, T., Yuan, L., & Zhang, J. B. (2021). Scene perception based visual navigation of mobile robot in indoor environment. ISA transactions, 109, 389-400. https://doi.org/10.1016/j.isatra.2020.10.023.
Saidi, Y., Nemra, A., & Tadjine, M. (2020). Robust mobile robot navigation using fuzzy type 2 with wheel slip dynamic modeling and parameters uncertainties. International Journal of Modelling and Simulation, 40(6), 397-420. https://doi.org/10.1080/02286203.2019.1646480.
Singh, P., Agrawal, P., Nandanwar, A., Behera, L., Verma, N. K., Nahavandi, S., & Jamshidi, M. (2020). Multivariable event-triggered generalized super-twisting controller for safe navigation of nonholonomic mobile robot. IEEE Systems Journal, 15(1), 454-465. https://doi.org/10.1109/JSYST.2020.2985730.
Wang, D., Hu, Y., & Ma, T. (2020). Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia. Cognitive Systems Research, 59, 1-14. https://doi.org/10.1016/j.cogsys.2019.09.006.
Wang, H., Zhang, F., & Wang, L. (2020). Fruit classification model based on improved Darknet53 convolutional neural network. In 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 881-884). https://doi.org/10.1109/ICITBS49701.2020.00194.
Wang, X., Mizukami, Y., Tada, M., & Matsuno, F. (2021). Navigation of a mobile robot in a dynamic environment using a point cloud map. Artificial Life and Robotics, 26, 10-20. https://doi.org/10.1007/s10015-020-00617-3.
Zhou, L., Rao, X., Li, Y., Zuo, X., Qiao, B., & Lin, Y. (2022). A lightweight object detection method in aerial images based on dense feature fusion path aggregation network. ISPRS International Journal of Geo-Information, 11(3), 189. https://doi.org/10.3390/ijgi11030189.
BUI, T.-L., & TRAN, N.-T. (2023). NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT. Applied Computer Science, 19(2), 82–95. https://doi.org/10.35784/acs-2023-16
Authors
Thanh-Lam BUIHanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Viet Nam
Authors
Ngoc-Tien TRANtientn@haui.edu.vn
Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Viet Nam
https://orcid.org/0000-0001-5099-3758
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