Plano de trajetória para quadro de baixo custo através de software educacional
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Publicado:
Jun 2, 2018
Palavras-chave:
Ar.drone, Coordenadas, Software educacional, Quadrotor, Trajectory, Speed
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Resumo
Este artigo é publicado como documento de consulta para estudantes de graduação e pós-graduação em a engenharia aérea, que requer uma implementação de controle rápido e efetivo de um robô aéreo. Tem como objetivo apresentar o projeto e a implementação de um planejador de trajetórias para um quadrotor de baixo custo usado como objeto de pesquisa. Além disso, você descobrirá que uma conceituação da análise matemática necessária é feita correspondente aos quadro-tipo multirotors, a estratégia de controle, o cálculo do planejador de trajetória ponto-a-ponto com um perfil de velocidade trapezoidal construído em Software Educacional e mais tarde o analise dos resultados obtidos na prática ca através de uma validação experimental do mesmo na plataforma Ar.drone2 da empresa Parrot versus a simulação realizada. Estes resultados permitirão determinar a eficácia do planejador de trajetórias de acordo com as coordenadas coordenadas XYZ, Para esta investigação, a orientação do robô aéreo não foi interagida, ou seja, não há controle sobre a rotação em relação ao eixo Z.
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Como Citar
Cáceres, E. A. G. (2018). Plano de trajetória para quadro de baixo custo através de software educacional. Ingenio Magno, 8(2), 125 - 139. Recuperado de http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1506
Seção
Artículos Vol. 8-2
DECLARACIÓN DE ORIGINALIDAD DE ARTÍCULO PRESENTADO
Por medio del presente documento, certifico(amos) que el artículo que se presenta para posible publicación en la revista institucional INGENIO MAGNO del Centro de Investigaciones de Ingeniería Alberto Magno CIIAM de la Universidad Santo Tomás, seccional Tunja, es de mi (nuestra) entera autoría, siendo su contenido producto de mi (nuestra) directa contribución intelectual y aporte al conocimiento.
Todos los datos y referencias a publicaciones hechas están debidamente identificados con su respectiva nota bibliográfica y en las citas que se destacan como tal. De requerir alguna clase de ajuste o corrección, comunicaré(emos) de tal procedimiento con antelación a los responsables de la revista.
Por lo anteriormente expresado, declaro(amos) que el material presentado en su totalidad se encuentra conforme a la legislación aplicable en materia de propiedad intelectual e industrial de ser el caso, y por lo tanto, me(nos) hago (hacemos) absolutamente responsable(s) de cualquier reclamación relacionada a esta.
En caso que el artículo presentado sea publicado, manifiesto(amos) que cedo(emos) plenamente a la Universidad Santo Tomás, seccional Tunja, los derechos de reproducción del mismo.
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Khamseh, H. B., Pimenta, L. C. A., & Tôrres, L. A. B. (2014). Decentralized Coordination of Constrained Fixed-wing Unmanned Aerial Vehicles : Circular Orbits. IFAC Proceedings Volumes (Vol. 47). IFAC. https://doi.org/10.3182/20140824-6-ZA-1003.01643
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Lippiello, V., & Ruggiero, F. (2012). Cartesian Impedance Control of a UAV with a Robotic Arm. IFAC Proceedings Volumes (Vol. 45). IFAC. https://doi.org/10.3182/20120905-3-HR-2030.00158
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Amato, E. D., Francesco, G. Di, Notaro, I., Tar¬taglione, G., Mattei, M., Francesco, G. Di, Notaro, I. (2015). ScienceDirect Nonlinear Dy¬namic Inversion and Neural Networks for a Tilt Tri-Rotor UAV Nonlinear Dynamic Inversion and Neural Networks for a Tilt Tri-Rotor UAV Non¬linear Dynamic Dynamic Inversion and Neural Neural Networks for a a Tilt Tilt Nonlinear Inve. IFAC-PapersOnLine, 48(9), 162–167. https://doi.org/10.1016/j.ifacol.2015.08.077
Antonelli, G., Baizid, K., Caccavale, F., Giglio, G., & Pierri, F. (2014). CAVIS: a Control software Architecture for cooperative multi-unmanned aerial VehIcle-manipulator Systems. IFAC Pro¬ceedings Volumes (Vol. 47). IFAC. https://doi. org/10.3182/20140824-6-ZA-1003.02366
Ayanian, N., Rus, D., & Kumar, V. (2012). Decentralized Multirobot Control in Partially Known Environments with Dynamic Task Reassignment. IFAC Proceedings Volumes (Vol. 45). IFAC. https://doi. org/10.3182/20120914-2-US-4030.00029
Behera, L. (2014). International Federation of Automatic Control 3 rd International Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2014, (March), 13–15. https://doi. org/10.3182/20140313-3-IN-3024.90001
Bouktir, Y., Haddad, M., & Chettibi, T. (2008). Trajectory planning for a quadrotor helicopter, 1258–1263.
Brown, T. B., Cheng, R., Sirault, X. R. R., Run¬grat, T., Murray, K. D., Trtilek, M., Borevitz, J. O. (2014). TraitCapture: Genomic and en¬vironment modelling of plant phenomic data Current Opinion in Plant Biology, 18(1), 73–79. https://doi.org/10.1016/j.pbi.2014.02.002
Caccavale, F., Giglio, G., Muscio, G., & Pie¬rri, F. (2014). Adaptive control for UAVs equipped with a robotic arm. IFAC Proceedings Volumes (Vol. 47). IFAC. https://doi.org/10.3182/20140824-6-ZA-1003.00790
Drone, A. R., & Quadrotor, D. (2016). Scien¬ceDirect with with. IFAC-PapersOnLine, 49(6), 236–241. https://doi.org/10.1016/j.ifacol.2016.07.183
Duan, T., Chapman, S. C., Guo, Y., & Zheng, B. (2017). Field Crops Research Dynamic moni¬toring of NDVI in wheat agronomy and bree¬ding trials using an unmanned aerial vehicle. Field Crops Research, 210(June), 71–80. https://doi.org/10.1016/j.fcr.2017.05.025
François, P. B., David, C., & Jemmapes, D. (2011). The Navigation and Control technology inside the AR . Drone micro UAV. IFAC Proceedings Volumes (Vol. 44). IFAC. https://doi.org/10.3182/20110828-6-IT-1002.02327
Freeman, P. K., & Freeland, R. S. (2014). Politics & technology : U . S . polices restricting unmanned aerial systems in agriculture, 49, 302–311. https://doi.org/10.1016/j.foodpol.2014.09.008
Gabrlik, P., Gabrlik, P., & Gabrlik, P. (2015). ScienceDirect The Use of Direct Georeferen-cing in Aerial The Use in The Photogrammetry Use of of Direct Direct Georeferencing Georeferencing in Aerial Aerial with Micro UAV Photogrammetry with UAV Photogrammetry with. IFAC-PapersOnLine, 48(4), 380–385. https://doi.org/10.1016/j.ifacol.2015.07.064
Garrido, M., Paraforos, D. S., Reiser, D., Arellano, M. V., Griepentrog, H. W., & Valero, C. (2015). 3D maize plant reconstruction based on georeferenced overlapping lidar point clouds. Remote Sensing, 7(12), 17077– 17096. https://doi.org/10.3390/rs71215870
Gohardani, O., Chapartegui, M., & Elizetxea, C. (2014). Progress in Aerospace Sciences Potential and prospective implementation of carbon nanotubes on next generation aircraft and space vehicles : A review of current and expected applications in aerospace sciences. Progress in Aerospace Sciences, 70, 42–68. https://doi.org/10.1016/j.paerosci.2014.05.002
GPSPRO. (n.d.). SIA "Infinitas" - Parrot Droni - Parrot AR.Drone 2.0 orange + Gloves and 16GB USB Stick Parrot Dro¬ni. Retrieved October 11, 2017, from http://www.gpspro.lv/products/lv/326/4662/sort/1/filter/0_0_0_0/Parrot-AR.Drone-2.0-orange- --Gloves-and-16GB-USB-Stick-Parrot-Droni.html
Guenard, A., & Ciarletta, L. (2012). The AE¬TOURNOS project : Using a flock of UAVs as a Cyber Physical System and platform for application-driven research, 10(EmSens), 939–945. https://doi.org/10.1016/j.procs.2012.06.127
Guerrero, J. S., Contreras, A. F., Hernández, J. I., & Neira, L. A. (2015). Instrumentation of an Array of Ultrasonic Sensors and Data Processing for Unmanned Aerial Vehicle ( UAV ) for Teaching the Application of the Kalman Filter. Procedia - Procedia Computer Science, 75(Vare), 375–380. https://doi.org/10.1016/j.procs.2015.12.260
Hehn, M., & Andrea, R. D. (2011). Quadrocopter Trajectory Generation and Control. IFAC Proceedings Volumes (Vol. 44). IFAC. https://doi.org/10.3182/20110828-6-IT-1002.03178
Jiang, B., Bishop, A. N., Anderson, B. D. O., & Drake, S. P. (2014). Path Planning for Minimizing Detection. IFAC Procee¬dings Volumes (Vol. 47). IFAC. https://doi. org/10.3182/20140824-6-ZA-1003.00634
Kapitonov, A. A. (2014). Geometric path following control of a rigid body based on the stabilization of sets. IFAC Proceedings Volumes (Vol. 47). IFAC. https://doi.org/10.3182/20140824-6-ZA-1003.02502
Khamseh, H. B., Pimenta, L. C. A., & Tôrres, L. A. B. (2014). Decentralized Coordination of Constrained Fixed-wing Unmanned Aerial Vehicles : Circular Orbits. IFAC Proceedings Volumes (Vol. 47). IFAC. https://doi.org/10.3182/20140824-6-ZA-1003.01643
King, S., Lai, S., Wang, F., Lan, M., & Chen, B. M. (2015). Mechatronics Systems design and implementation with jerk-optimized trajectory generation for UAV calligraphy. Mecha-tronics, 30, 65–75. https://doi.org/10.1016/j.mechatronics.2015.06.006
Li, Q., Li, D., Wu, Q., Tang, L., & Huo, Y. (2013). Computers in Industry Autonomous navigation and environment modeling for MAVs in 3-D enclosed industrial environments. Computers in Industry, 64(9), 1161–1177. https://doi.org/10.1016/j.compind.2013.06.010
Lippiello, V., & Ruggiero, F. (2012). Cartesian Impedance Control of a UAV with a Robotic Arm. IFAC Proceedings Volumes (Vol. 45). IFAC. https://doi.org/10.3182/20120905-3-HR-2030.00158
Luo, Y., Guan, T., Wei, B., Pan, H., & Yu, J. (2015). Neurocomputing Fast terrain mapping from low altitude digital imagery. Neurocomputing, 156, 105–116. https://doi.or-g/10.1016/j.neucom.2014.12.079
Matouk, D., & Gherouat, O. (2016). Quadrotor Position and Attitude Control via Backstepping Approach, 73–79.
Modeling, Estimation, and Control of Quadrotor. (2012), (August). Mu, A., Parra-vega, V., & Anand, S. (2015). ScienceDirect Control Control Control Control Control. IFAC-PapersOnLine, 48(19), 118–123. https://doi.org/10.1016/j.ifacol.2015.12.020
Olivares, V., Cordova, F., Sepúlveda, J. M., & Derpich, I. (2015). Information Technology and Quantitative Management ( ITQM 2015 ) MODELING INTERNAL LOGISTICS BY USING DRONES ON THE STAGE OF ASSEMBLY OF PRODUCTS. Procedia - Procedia Computer Science, 55(Itqm), 1240–1249. https://doi.org/10.1016/j.procs.2015.07.132
Önder, M., Eresen, A., & Imamog, N. (2012). Expert Systems with Applications Autono-mous quadrotor flight with vision-based obstacle avoidance in virtual environment, 39, 894–905. https://doi.org/10.1016/j.eswa.2011.07.087
Palunko, I., & Fierro, R. (2011). Adaptive Control of a Quadrotor with Dynamic Changes in the Center of Gravity. IFAC Proceedings Volumes (Vol. 44). IFAC. https://doi.org/10.3182/20110828-6-IT-1002.02564
Pounds, P., Mahony, R., & Corke, P. (2010). Control Engineering Practice Modelling and control of a large quadrotor robot. Control Engineering Practice, 18(7), 691–699. https://doi.org/10.1016/j.conengprac.2010.02.008
Pretorius, A., & Boje, E. (2014). De¬sign and Modelling of a Quadrotor Helicopter with Variable Pitch Rotors for Aggressive Manoeuvres. IFAC Proceedings Volumes (Vol. 47). IFAC. https://doi. org/10.3182/20140824-6-ZA-1003.01586
Raffler, T., Wang, J., & Holzapfel, F. (2013). Path Generation and Control for Unmanned Multirotor Vehicles Using Nonlinear Dynamic Inversion and Pseudo Control Hedging. IFAC Proceedings Volumes (Vol. 46). IFAC. https://doi.org/10.3182/20130902-5-DE-2040.00132
Riccardi, F., Farooq, M., Formentin, S., Lovera, M., Elettronica, D., Bioingegneria, I., Leonardo, P. (2013). Control of variable-pitch quadrotors. IFAC Proceedings Volumes (Vol. 46). IFAC. https://doi. org/10.3182/20130902-5-DE-2040.00143
Rocchi, A. B. C. A. P. C. (2008). Hierarchical and Hybrid Model Predictive Control of Quadcopter Air Vehicles. IFAC Proceedings Volumes (Vol. 42). IFAC. https://doi.org/10.3182/20090916-3-ES-3003.00004
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