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Warianty tytułu
Języki publikacji
Abstrakty
The capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. 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. When the light illumination increases from 300 lux to 1000 lux, the reliability of the recognition model on different objects also improves, from about 75% to 98%, respectively. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
82--95
Opis fizyczny
Bibliogr. 15 poz., fig., tab.
Twórcy
autor
- Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam
autor
- Hanoi University of Industry, Faculty of Mechanical Engineering, Department of Mechatronics Engineering, Vietnam
Bibliografia
- [1] Abdoun, O. & Abouchabaka, J. (2011). A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem. arXiv. https://doi.org/10.48550/arXiv.1203.3097
- [2] Davis, L. (1985). Applying Adaptive Algorithms to Epistatic Domains. Proceedings of the 9th International Joint Conference on Artificial Intelligence, 1, 162-164. https://dl.acm.org/doi/10.5555/1625135.1625164
- [3] Eiben, A.E., & Smith, J.E. (2015). Fitness, Selection, and Population Management. Introduction to Evolutionary Computing (pp. 79–98). Springer. https://doi.org/10.1007/978-3-662-44874-8_5
- [4] Goldberg, D. & Lingle, R. (1985). Alleles, Loci and the Traveling Salesman Problem. Proceedings of the 1st International Conference on Genetic Algorithms and Their Applications, 154-159. https://dl.acm.org/doi/10.5555/645511.657095
- [5] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston. Addison-Wesley Publishing Company. https://dl.acm.org/doi/book/10.5555/534133
- [6] Grefenstette, J. J. (1986). Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1), 122-128. https://doi.org/10.1109/TSMC.1986.289288
- [7] Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor: University of Michigan Press. https://doi.org/10.7551/mitpress/1090.001.0001
- [8] Liao, Y. F., Yau, D. H., & Chen, C. L. (2012). Evolutionary algorithm to traveling salesman problems. Computers & Mathematics with Applications, 64(5), 788-797. https://doi.org/10.1016/j.camwa.2011.12.018
- [9] Dry, M., Lee, M. D., Vickers, D., & Hughes, P. (2006). Human Performance on Visually Presented Traveling Salesperson Problems with Varying Numbers of Nodes. The Journal of Problem Solving, 1(1). https://doi.org/10.7771/1932-6246.1004
- [10] Mousa, A. A., El-Shorbagy, M. A. & Farag, M. A. (2017). K-means-Clustering Based Evolutionary Algorithm for Multi-objective Resource Allocation Problems. Applied Mathematics & Information Sciences. 11(6), 1681-1692. https://doi.org/10.18576/amis/110615
- [11] Oliver, I. M., Smith, D. j., & Holland, J. R. C. (1987). A Study of Permutation Crossover Operators on the Traveling Salesman Problem. International Conference on Genetic Algorithms. 224-230. https://dl.acm.org/doi/abs/10.5555/42512.42542
- [12] Macgregor, J. N., & Ormerod, T. (1996). Human performance on the traveling salesman problem. Perception & Psychophysics, 58(4), 527–539. https://doi.org/10.3758/BF03213088
- [13] Silberholz J., Golden B. (2007), The Generalized Traveling Salesman Problem: A New Genetic Algorithm Approach, In Baker, E.K., Joseph, A., Mehrotra, A., Trick, M.A. (Eds), Extending the Horizons: Advances in Computing, Optimization, and Decision Technologies, 37. Springer 165–181. https://doi.org/10.1007/978-0-387-48793-9_11
- [14] Yu, F., Fu, X., Li, H., & Dong, G. (2016). Improved Roulette Wheel Selection-Based Genetic Algorithm for TSP. 2016 Insternational Conference on Network and Information Systems for Computers (ICNISC) (151-154). https://doi.org/10.1109/icnisc.2016.041
- [15] Yu, X., & Gen, M. (2010). Introduction to Evolutionary Algorithms. Decision Engineering. (pp. 286–288) Springer. https://doi.org/10.1007/978-1-84996-129-5
Typ dokumentu
Bibliografia
Identyfikator YADDA
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