Trajectory planning for low cost quadrotor through an educational software
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Published:
Jun 2, 2018
Keywords:
Ar.drone, coordinates, educational software, Quadrotor, trajectory, speed
Main Article Content
Abstract
This article is presented as a consultation paper for undergraduate and postgraduate students in the engineering airfield, which requires an implementation of fast and efficient control of an aerial robot. It aims tat presenting the design and implementation of a trajectory planner for a low cost quadrotor used as a research object. In addition, you will find that a conceptualization of the necessary mathematical analysis is made corresponding to the quadrotor-type multirotors, the control strategy, the calculation of the point-to-point trajectory planner with a trapezoidal velocity profile built in educational software and later the analysis of the results obtained in practice through an experimental validation of the same in the platform Ar.drone2 of the company Parrot vs the simulation carried out. These results will allow determining the effectiveness of the trajectory planner according to the computed coordinates XYZ, for this investigation it was not interacted with the orientation of the aerial robot, that is, there is no control over the rotation with respect to the Z-axis.
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Cáceres, E. A. G. (2018). Trajectory planning for low cost quadrotor through an educational software. Ingenio Magno, 8(2), 125 - 139. Retrieved from http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1506
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Artículos Vol. 8-2
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With this document, I/We certify that the article submitted for possible publication in the institutional journal INGENIO MAGNO of the Research Center Alberto Magno CIIAM of the University Santo Tomás, Tunja campus, is entirely of my(our) own writing, and is a product of my(our) direct intellectual contribution to knowledge.
All data and references to completed publications are duly identified with their respective bibliographical entries and in the citations thus highlighted. If any adjustment or correction is needed, I(we) will contact the journal authorities in advance.
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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
Rossi, C., & Savino, S. (2013). Robot trajectory planning by assigning positions and tan-gential velocities. Robotics and Computer-Integrated Manufacturing, 29(1), 139–156. https://doi.org/10.1016/j.rcim.2012.04.003
Rudolph, M. K. J. (2013). Quadrotor tracking control based on a moving frame. IFAC Pro-ceedings Volumes (Vol. 46). IFAC. https://doi.org/10.3182/20130904-3-FR-2041.00142
Saska, M., Kasl, Z., & P, L. (2014). Motion planning and control of formations of micro aerial vehicles, 1228–1233. https://doi. org/10.3182/20140824-6-ZA-1003.02295
Sumano, E., Castro, R., & Salazar, R. L. S. (2013). Synchronized Flight Formation of Quadrotors. IFAC Proceedings Volumes (Vol. 46). IFAC. https://doi.org/10.3182/20131120-3-FR-4045.00042
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