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This paper discusses the use of the D-star algorithm to construct an optimal route for a mobile robot in a space with obstacles. We present a mathematical description of the D-star algorithm operating principle, which is based on the idea of dynamic programming and step-by-step path cost updating. Based on this description, a Python program was developed that is capable of building a route for the robot, taking into account the situation around it. To test the efficiency and accuracy of the algorithm, a number of experiments were carried out on various test maps with different obstacle configurations. The results showed that the D-star algorithm demonstrates high efficiency and reliability in constructing the optimal route for a mobile robot under various conditions.

  • O'qishlar soni 105
  • Nashr sanasi 01-04-2024
  • Asosiy tilIngliz
  • Sahifalar12
English

This paper discusses the use of the D-star algorithm to construct an optimal route for a mobile robot in a space with obstacles. We present a mathematical description of the D-star algorithm operating principle, which is based on the idea of dynamic programming and step-by-step path cost updating. Based on this description, a Python program was developed that is capable of building a route for the robot, taking into account the situation around it. To test the efficiency and accuracy of the algorithm, a number of experiments were carried out on various test maps with different obstacle configurations. The results showed that the D-star algorithm demonstrates high efficiency and reliability in constructing the optimal route for a mobile robot under various conditions.

Русский

В данной статье рассматривается использование алгоритма D-star для построения оптимального маршрута мобильного робота в пространстве с препятствиями. Представлено математическое описание принципа работы алгоритма D-star, основанного на идее динамического программирования и пошагового обновления стоимости пути. На основе этого описания была разработана программа на Python, способная строить маршрут для робота с учетом ситуации вокруг него. Для проверки эффективности и точности алгоритма был проведен ряд экспериментов на различных тестовых картах с разной конфигурацией препятствий. Результаты показали, что алгоритм D-star демонстрирует высокую эффективность и надежность при построении оптимального маршрута мобильного робота в различных условиях.

Ўзбек

Ushbu maqolada to'siqlar bo'lgan fazoda mobil robot uchun optimal marshrutni qurish uchun D-yulduz algoritmidan foydalanish muhokama qilinadi. Biz dinamik dasturlash va bosqichma-bosqich yo'l xarajatlarini yangilash g'oyasiga asoslangan D-yulduz algoritmining ishlash printsipining matematik tavsifini taqdim etamiz. Ushbu tavsif asosida robot atrofidagi vaziyatni hisobga olgan holda marshrutni qurishga qodir bo'lgan Python dasturi ishlab chiqildi. Algoritmning samaradorligi va aniqligini tekshirish uchun turli xil to'siqlar konfiguratsiyasiga ega bo'lgan turli sinov xaritalarida bir qator tajribalar o'tkazildi. Natijalar shuni ko'rsatdiki, D-star algoritmi turli sharoitlarda mobil robot uchun optimal marshrutni qurishda yuqori samaradorlik va ishonchlilikni namoyish etadi.

Havola nomi
1 1. Attar, H., Abu-Jassar, A. T., Amer, A., Lyashenko, V., Yevsieiev, V., & Khosravi, M. R. (2022). Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging. Computational intelligence and neuroscience, 2022, 9140156.
2 2. Nevliudov, I., Yevsieiev, V., Baker, J. H., Ahmad, M. A., & Lyashenko, V. (2020). Development of a cyber design modeling declarative Language for cyber physical production systems. J. Math. Comput. Sci., 11(1), 520-542.
3 3. Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal, 10(4), 1645.
4 4. Matarneh, R., Maksymova, S., Deineko, Z., & Lyashenko, V. (2017). Building robot voice control training methodology using artificial neural net. International Journal of Civil Engineering and Technology, 8(10), 523-532.
5 5. Matarneh, R., Maksymova, S., Lyashenko, V. V., & Belova, N. V. (2017). Speech Recognition Systems: A Comparative Review. IOSR Journal of Computer Engineering (IOSR-JCE), 19(5), 71-79.
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