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Stan wiedzy na temat rozwoju prototypu autonomicznego modelu poruszającego się pojazdu sterowanego przez mikrokomputer
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Abstrakty
The development of novel digital technologies and the fast development of electric drive systems for the transport industry leads to the necessity of creating novel educational equipment to train students in developing control algorithms, hardware, and software for controlling the movement of electric vehicles. One of the solutions is implementing a simple and cheap single-board computer for this task. This work deals with developing the prototype of a small-sized electric vehicle model based on the single-board computer Raspberry Pi to create a laboratory basis for teaching students to use novel technologies in controlling electromechanical equipment.
Rozwój nowoczesnych technologii cyfrowych oraz szybki rozwój elektrycznych układów napędowych dla przemysłu transportowego powoduje konieczność tworzenia nowatorskich urządzeń edukacyjnych do szkolenia studentów w zakresie opracowywania algorytmów sterowania, sprzętu i oprogramowania do sterowania ruchem pojazdów elektrycznych. Jednym z rozwiązań jest wdrożenie do tego zadania prostego i taniego komputera jednopłytowego. Niniejsza praca dotyczy opracowania prototypu modelu małogabarytowego pojazdu elektrycznego w oparciu o komputer jednopłytkowy Raspberry Pi w celu stworzenia bazy laboratoryjnej do nauczania studentów wykorzystania nowatorskich technologii w sterowaniu urządzeniami elektromechanicznymi.
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Czasopismo
Rocznik
Tom
Strony
78--81
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
- Turiba University, Graudu Street 68, LV-1058 Riga, Latvia, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600, Kremenchuk, Ukraine
autor
- Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
autor
- Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600, Kremenchuk, Ukraine
Bibliografia
- [1] Badue, C., et al., Self-driving cars: A survey, Expert Systems with Applications, 2021, 165, 113816
- [2] Ni, J.; Chen, Y.; Chen, Y.; Zhu, J.; Ali, D.; Cao, W. A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods. Appl. Sci. 2020, 10, 2749. https://doi.org/10.3390/app10082749
- [3] M. Daily, S. Medasani, R. Behringer and M. Trivedi, "Self-Driving Cars," in computer, vol. 50, no. 12, pp. 18-23, December 2017, doi: 10.1109/MC.2017.4451204.
- [4] S. Karnouskos, "Self-Driving Car Acceptance and the Role of Ethics," in IEEE Transactions on Engineering Management, vol. 67, no. 2, pp. 252-265, May 2020, doi: 10.1109/TEM.2018.2877307.
- [5] Nyholm, S., Smids, J. The Ethics of Accident-Algorithms for Self-Driving Cars: an Applied Trolley Problem?. Ethic Theory Moral Prac 19, 1275–1289 (2016). https://doi.org/10.1007/s10677-016-9745-2
- [6] Stilgoe, J. How can we know a self-driving car is safe?. Ethics Inf Technol 23, 635–647 (2021). https://doi.org/10.1007/s10676-021-09602-1
- [7] Stilgoe J. Machine learning, social learning and the governance of self-driving cars. Social Studies of Science. 2018;48(1):25-56. doi:10.1177/0306312717741687
- [8] Deruyttere T., Milewski V., Moens M.-F., Giving commands to a self-driving car: How to deal with uncertain situations?, Engineering Applications of Artificial Intelligence, Volume 103, 2021, 104257, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2021.104257.
- [9] Davnall, R. Solving the Single-Vehicle Self-Driving Car Trolley Problem Using Risk Theory and Vehicle Dynamics. Sci Eng Ethics 26, 431–449 (2020). https://doi.org/10.1007/s11948-019-00102-6
- [10] Vellinga N.E., From the testing to the deployment of self driving cars: Legal challenges to policymakers on the road ahead, Computer Law & Security Review, Volume 33, Issue 6, 2017, Pages 847-863, ISSN 0267-3649, https://doi.org/10.1016/j.clsr.2017.05.006.
- [11] Lin, M.; Yoon, J.; Kim, B. Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter. Sensors 2020, 20, 2544. https://doi.org/10.3390/s20092544
- [12] Fathy M., Ashraf N., Ismail O., Fouad S., Shaheen L., Hamdy A., Design and implementation of self-driving car, Procedia Computer Science, Volume 175, 2020, Pages 165-172, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.07.026.
- [13] Kanagaraj, N., Hicks, D., Goyal, A. et al. Deep learning using computer vision in self driving cars for lane and traffic sign detection. Int J Syst Assur Eng Manag 12, 1011–1025 (2021). https://doi.org/10.1007/s13198-021-01127-6
- [14] Gupta A., Anpalagan A., Guan L., Khwaja A. S., Deep learning for object detection and scene perception in self driving cars: Survey, challenges, and open issues, Array, Volume 10, 2021, 100057, ISSN 2590-0056, https://doi.org/10.1016/j.array.2021.100057.
- [15] Ni J., Shen K., Chen Y., Cao W. and Yang S. X., "An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-14, 2022, Art no. 5001614, doi: 10.1109/TIM.2022.3146923.
- [16] Gaiduk, B. Gurenko, E. Plaksienko, I. Shapovalov, M. Beresnev, Development of Algorithms for Control of Motor Boat as Multidimensional Nonlinear Object,MATEC Web of Conferences, Vol. 34, 2015, http://dx.doi.org/10.1051/matecconf/20153404005
- [17] Pshikhopov, V., Medvedev, M., Gurenko, B., Beresnev, M. Basic algorithms of adaptive position path control systems for mobile units ICCAS 2015 — 2015 15th International Conference on Control, Automation and Systems, Proceedings 23 December 2015, Article number 7364878, Pages 54-59 DOI: 10.1109/ICCAS.2015.7364878
- [18] Pshikhopov, M. Medvedev, V. Krukhmalev,V. Shevchenko Base Algorithms of the Direct Adaptive Position-Path Control for Mobile Objects Positioning. Applied Mechanics and Materials Vol. 763 (2015) pp 110-119 © (2015) Trans Tech Publications, Switzerland. doi:10.4028/www.scientific.net/AMM.763.110
- [19] Fedorenko, B. Gurenko, Local and Global Motion Planning for Unmanned Surface Vehicle, MATEC Web of Conferences, Vol. 45, 2016, doi: http://dx.doi.org/10.1051/matecconf/20164201005
- [20] Gurenko, R. Fedorenko, A. Nazarkin, Autonomous Surface Vehicle Control System, Applied Mechanics and Materials, Vols 704, pp. 277-282, 2015, doi: 10.4028/www.scientific.net/AMM.704.277
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3409cbb1-7df7-4ea2-a126-54c29d5a86bb