Crazyflie como plataforma educativa Innovando la formación en Automática
Barra lateral del artículo
Contenido principal del artículo
Resumen
La significativa evolución y mejora de los drones ha impulsado su uso como plataforma de experimentación en el campo de la Automática, tanto en educación como en investigación, destacándose su modularidad y versatilidad. Este artículo ofrece una revisión de las principales configuraciones posibles con los drones Crazyflie de Bitcraze, una plataforma flexible y con muchas posibilidades para la formación en Automática. Su diseño compacto facilita la integración de nuevos sensores y módulos, así como la explicación de sistemas de posicionamiento como Lighthouse y Loco Positioning. También se introducen las implementaciones de controladores PID para garantizar la estabilidad y control del vuelo, que son modificables por el alumnado. Además, se analizan los beneficios de usar drones en entornos educativos, mejorando tanto la enseñanza práctica como teórica en Automática. En resumen, este estudio reconoce el impacto transformador de los drones en la educación en Automática y destaca su papel en la innovación educativa, creando un entorno académico más dinámico y atractivo.
Palabras clave:
Detalles del artículo
Citas
Avadhanula, R., 2023. Cooperative collision avoidance on small-sized quadcopters with indoor loco positioning system.
Bitcraze, 2024. Controllers in the Crazyflie. https://www.bitcraze.io/documentation/repository/crazyflie-firmware/master/functional-areas/sensor-to-control/controllers/, retrieved: 2024-05-25.
Bitcraze, 2024. CrazyFlie Expansion Decks. https://www.bitcraze.io/documentation/tutorials/getting-started-with-expansion-decks/, retrieved: 2024-05-25.
Bitcraze, 2024. Datasheet Crazyflie 2.1. https://www.bitcraze.io/products/crazyflie-2-1/, retrieved: 2024-05-25.
Bitcraze, 2024. Getting started with the Crazyflie 2.X. https://www.bitcraze.io/documentation/tutorials/getting-started-with-crazyflie-2-x/, retrieved: 2024-05-25.
Bitcraze, 2024. Getting started with the Loco Positioning System. https://www.bitcraze.io/documentation/tutorials/getting-started-with-loco-positioning-system/, retrieved: 2024-05-25.
Bitcraze, 2024. Lighthouse Positioning System. https://www.bitcraze.io/documentation/system/positioning/ligthouse-positioning-system/, retrieved: 2024-05-25.
Bitcraze, 2024. Loco Positioning system. https://www.bitcraze.io/documentation/system/positioning/loco-positioning-system/, retrieved: 2024-05-25.
Bitcraze, 2024. Motion Capture Positioning. https://www.bitcraze.io/documentation/system/positioning/mocap-positioning/, retrieved: 2024-05-25.
Bitcraze, 2024. Positioning Systems Overview. https://www.bitcraze.io/documentation/system/positioning/, retrieved: 2024-05-25.
Chu, T. S., Chua, A., Sybingco, E., Roque, M. A., 2022. A performance analysis on drone loco positioning system for two-way ranging protocol. ASEAN Engineering Journal 12 (3), 95–102. DOI: 10.11113/aej.v12.17487 DOI: https://doi.org/10.11113/aej.v12.17487
Fernando, M., Liu, L., 2019. Formation control and navigation of a quadrotor swarm. In: 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 284–291. DOI: 10.1109/ICUAS.2019.8798352 DOI: https://doi.org/10.1109/ICUAS.2019.8798352
Giernacki, W., Skwierczy´nski, M., Witwicki, W., Wro´nski, P., Kozierski, P., 2017. Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, pp. 37–42. DOI: https://doi.org/10.1109/MMAR.2017.8046794
Gün, A., 2023. Attitude control of a quadrotor using pid controller based on differential evolution algorithm. Expert Systems with Applications 229, 120518. DOI: 10.1016/j.eswa.2023.120518 DOI: https://doi.org/10.1016/j.eswa.2023.120518
Jong-Hwan, B., Myeong-Suk, P., Sang-Hoon, K., 2017. Design of docking drone system using p-pid flight controller. Advances in Computer Science and Ubiquitous Computing 421, 768–773. DOI: 10.1007/978-981-10-3023-9118 DOI: https://doi.org/10.1007/978-981-10-3023-9_118
Kilberg, B. G., Campos, F. M. R., Schindler, C. B., Pister, K. S. J., JUL 2020. Quadrotor-based lighthouse localization with time-synchronized wireless sensor nodes and bearing-only measurements. Sensors 20 (14), 3888. DOI: 10.3390/s20143888 DOI: https://doi.org/10.3390/s20143888
Leong, X. W., Hesse, H., 2019. Vision-based navigation for control of micro aerial vehicles. Proceedings of the 4th Irc Conference on Science, Engineering and Technology, Irc-Set 2018, 413–427. DOI: 10.1007/978-981-32-9828-633 DOI: https://doi.org/10.1007/978-981-32-9828-6_33
Naranjo, M., Fuentes, D., Muelas, E., Diez, E., Ciruelo, L., Alonso, C., Abenza, E., Gomez-Espinosa, R., Luengo, I., FEB 2023. Object detection-based system for traffic signs on drone-captured images. Drones 7 (2), 112. DOI: 10.3390/drones7020112 DOI: https://doi.org/10.3390/drones7020112
Neumann, P. P., Hirschberger, P., Baurzhan, Z., Tiebe, C., Hofmann, M., Huellmann, D., Bartholmai, M., 2019. Indoor air quality monitoring using flying nanobots: Design and experimental study. 2019 Ieee International Symposium on Olfaction and Electronic Nose (Isoen 2019), 1–3. DOI: https://doi.org/10.1109/ISOEN.2019.8823496
Noordin, A., Mohd Basri, M. A., Mohamed, Z., 2023. Real-time implementation of an adaptive pid controller for the quadrotor mav embedded flight control system. Aerospace 10 (1), 59. DOI: 10.3390/aerospace10010059 DOI: https://doi.org/10.3390/aerospace10010059
Pichierri, L., Testa, A., Notarstefano, G., AUG 2023. Crazychoir: Flying swarms of crazyflie quadrotors in ros 2. Ieee Robotics and Automation Letters 8 (8), 4713–4720. DOI: https://doi.org/10.1109/LRA.2023.3286814
Preiss, J. A., Honig, W., Sukhatme, G. S., Ayanian, N., 2017. Crazyswarm: A large nano-quadcopter swarm. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3299–3304. DOI: 10.1109/ICRA.2017.7989376 DOI: https://doi.org/10.1109/ICRA.2017.7989376
Punpigul, N., Thammawichai, M., 2019. A flight formation control of a micro aerial vehicle swarm using a motion capture. In: 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, pp. 128–131. DOI: 10.1109/ECTI-CON47248.2019.8955148 DOI: https://doi.org/10.1109/ECTI-CON47248.2019.8955148
Puri, V., Nayyar, A., Raja, L., 2017. Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics Management Systems 20 (4), 507–518. DOI: 10.1080/09720510.2017.1395171 DOI: https://doi.org/10.1080/09720510.2017.1395171
Sun, J.-h., Cheng, L. L., 2017. Robust pid controller for ar drone. Computer Science and Technology (Cst2016), 1213–1221. DOI: https://doi.org/10.1142/9789813146426_0138
Xin, C., Zhang, W., Yang, Q., 2020. Research and application prospect of pid auto-tuning. Proceedings of the 39th Chinese Control Conference, 5991–5995. DOI: 10.23919/ccc50068.2020.9188615 DOI: https://doi.org/10.23919/CCC50068.2020.9188615
Zekry, O. H., Ashry, M., Hafez, A., Attia, T., 2024. Integral-backstepping for crazyflie quadrotor trajectory tracking control. AIAA SCITECH 2024 Forum, 1710; 1710–1710. DOI: https://doi.org/10.2514/6.2024-1710
Zekry, O. H., Attia, T., Hafez, A. T., Ashry, M. M., 2023. Pid trajectory tracking control of crazyflie nanoquadcopter based on genetic algorithm. In: 2023 IEEE Aerospace Conference. IEEE, pp. 1–8. DOI: 10.1109/AERO55745.2023.10115538 DOI: https://doi.org/10.1109/AERO55745.2023.10115538