Алгоритмы управления летающим роботом при слежении за подвижным объектом

Developed control algorithms Flying Robot (quadrocopter), solves the problem of formation control commands for the flight by arbitrarily defined path. The variants quadrocopter tracking of moving objects

Программное обеспечение управлением движения БЛА на базе микроконтроллера Arduino

Объектом исследования является система управления тангажом беспилотного летательного аппарата 700AL-X6 Цель работы – разработать программное обеспечение управления тангажом БПЛА на базе микроконтроллера Arduino UNO. В ходе исследования был изучен состав, принципы и органы управления БПЛА 700AL-X6. При исследовании компонентов движения было принято решение отработать методику управления на основе компонента тангаж с использованием микроконтроллера Arduino UNO и показаний дальномера. В результате исследования написана программа управления тангажом, обеспечивающая обмен данными между микроконтроллером и датчиком расстояния, формирование сигналов ШИМ. Также проведены натурные эксперименты по управлению полетом БПЛА. Областью применения является лаборатория кафедры АиКС.The object of the study is a control system for the unmanned aircraft 700AL-X6 The goal of the work is to develop a pitch management software for UAVs based on the Arduino UNO microcontroller. In the course of the study, the composition, principles and controls of the UAV 700AL-X6 were studied. In the study of the components of motion, it was decided to develop a control method based on the pitch component using the Arduino UNO microcontroller and the range finder readings. As a result of the study, a pitch control program was written that provides data exchange between the microcontroller and the distance sensor, the formation of PWM signals. Also conducted in-depth experiments on flight control UAV. The field of application is the laboratory of the Department of AICS

RIAI - Revista Iberoamericana de Automatica e Informatica Industrial

[EN] Unmanned Aerial Vehicles (UAV) have oered alternatives for applications in which human integrity is compromised. In this sense, the need of increasing autonomy in these vehicles presents an alternative to artificial intelligence areas to enhance navigation capacities through several environments. This article presents an evaluation for estimating inclination and orientation, using automatic learning algorithms for a dynamic multi-rotor plant. To do so, an experiment is proposed to collect the data from multiple IMU sensors over an UAV main board, and submitted to dierent inclinations before achieving the classification task. The reported results using k nearest neighbors (k - NN), support vector machines (S VM) and Bayes show eficiency during the recognition, obtaining an accuracy score up to 99 %. Besides, the algoritms could be combined along with robust control techniques, which is ideal for implementation in embedded systems with low processing capacities.[ES] Los vehículos aéreos no tripulados (UAV) ofrecen alternativas para diversas aplicaciones en las que se compromete la integridad humana. En este sentido, la necesidad de incrementar la autonomía de estos vehículos presenta una alternativa al área de inteligencia artificial para aumentar las capacidades de navegación en diversos entornos. Este artículo presenta una evaluación para estimación de inclinación y orientación, utilizando algoritmos de aprendizaje automático para una planta dinámica con múltiples rotores. Para esto se propone un experimento para recopilar datos de unidades de medición inercial (IMU) sobre la placa de un UAV, y sometidos a diferentes inclinaciones antes de lograr la tarea de clasificación. Los resultados reportados usando los algoritmos de k vecinos más cercanos (k-NN), máquinas de soporte vectorial (SVM) y de Bayes muestran eficiencia en el reconocimiento, obteniendo una precisión hasta del 99 %. Además, estos algoritmos podrían combinarse con técnicas de control robustas, ideal para la implementación en sistemas con capacidades de procesamiento limitadas.Fonnegra, R.; Goez, G.; Tobón, A. (2019). Estimación de orientación de un vehículo aéreo no modelado usando fusión de sensores inerciales y aprendizaje de máquina. Revista Iberoamericana de Automática e Informática. 16(4):415-422. https://doi.org/10.4995/riai.2019.11286SWORD415422164Carabin, G., Vidoni, R., Mazzetto, F., Gasparetto, A., 2017. Dynamic model and instability evaluation of an articulated mobile agri-robot. In: Advances in Italian Mechanism Science. Springer, pp. 335-343. https://doi.org/10.1007/978-3-319-48375-7_36Castrillón, O. D., Giraldo, J. A., C, W. A. S., 2008. 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IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, 640-645. https://doi.org/10.1109/IROS.2010.5650249Razak, N. A., Arshad, N. H. M., Adnan, R., Misnan, M. F., Thamrin, N. M., Mahmud, S. F., 2013. A study of Kalman's filter in embedded controller for real-time quadrocopter roll and pitch measurement. Proceedings - 2012 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2012, 590-595. https://doi.org/10.1109/ICCSCE.2012.6487214Rish, I., 1999. An empirical study of the naive Bayes classifier. T.J. Watson Research Center, 41-46.Scholkopf, B., 2006. Smola, Learning with kernels : support vector machines, regularization, optimization, and beyond. No. February. https://doi.org/10.1017/CBO9781107415324.004Song, Y., Meng, Q. H., Luo, B., Zeng, M., Ma, S. G., Qi, P. F., 2016. 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