AN ANN-BASED INTEGRATED MODEL FOR AUTONOMOUS UAV FLIGHT CONTROL CONSIDERING EXTERNAL FORCES

Saewoong Min, Chulwoo Rhim, and Seongju Chang

References

  1. [1] S. Shadrin and A.I.A. Doroga, Analytical review of standardSAE J3016 taxonomy and definitions for terms relatedto driving automation systems for on-road motor vehicleswith latest updates, Avtomobil’. Doroga. InfrastrukturaAccessed: May 23, 2023. [Online]. Available: https://www.adi-madi.ru/madi/article/view/811?locale=en US
  2. [2] C. An, B. Li, W. Shi, and X. Zhang, “Autonomousquadrotor UAV systems for dynamic platform landing withonboard sensors, International Journal of Robotics andAutomation 2023, 38(6), 2004, 296–305, DOI: https://doi.org/10.2316/J.2023.206–0807.
  3. [3] X. Fu, and H. G uo, Robust adaptive fault-tolerant controlbased on GBF-CMAC neural network for low-altitudeUAV, International Journal of Robotics and Automation,38(4), 267–276, Accessed: Jul. 31, 2023. [Online]. Available:https://www.actapress.com/PDFViewer.aspx?paperId=55423
  4. [4] X. Ma, C. Tian, W. Chen, B. Peng, B. Peng, andX. Ma, “Low-complexity channel estimation and multi-user detection in MIMO-enabled UAV-assisted massive IoTaccess,” International Journal of Robotics and Automation,38(3), 231–240, Accessed: Jul. 31, 2023. [Online]. Available:https://www.actapress.com/PDFViewer.aspx?paperId=55436
  5. [5] Z. Zhou and X. Yang,Pine wilt disease detection in UAV-captured images, International Journal of Robotics and Auto-mation, 37, 37–43, Accessed: Jul. 31, 2023. [Online]. Available:https://m.actapress.com/PDFViewer.aspx?paperId=54800
  6. [6] Y. Yan, Z. Lv, J. Yuan, and S. Zhang Obstacle avoidancefor multi-UAV system with optimized artificial potential fieldalgorithm, International Journal of Robotics and Automation,36, 2021, 1–7, DOI: https://doi.org/10.2316/J.2021.206-0610.
  7. [7] Y. Yan, Z. Lv, P. Huang, J. Yuan, and H. Long Rapid selectingUAVs for combat based on three-way multiple attributedecision, International Journal of Robotics and Automation,36, 1–8, Accessed: Jul. 31, 2023. [Online]. Available:https://www.actapress.com/Abstract.aspx?paperId=51232
  8. [8] M. Moshref-Javadi and M. Winkenbach, Applications andresearch avenues for drone-based models in logistics: Aclassification and review, Expert System with Application, 177,2021, 114854.
  9. [9] A. Saha, J. Saha, R. Ray, S. S ircar, S. Dutta, S.P. Chat-topadhyay, and H.N. Saha IOT-based drone for improvementof crop quality in agricultural field, Proc. IEEE 8th AnnualComputing and Communication Workshop and Conference(CCWC), Las Vegas, 2023, 612–615. [Online]. Available:https://ieeexplore.ieee.org/abstract/document/8301662/?casatoken=OPSTb49DeJwAAAAA:6gpn-OUefWStf-skqzHPtcMfTqaNG84G wRMO yduOCsj0KP-WGMi-XRWu1cjzf2gHIFDps
  10. [10] S. Ahirwar, G. Namwade, R. Swarnkar, and S. Bhukya,Application of drone in agriculture, Journal of CurrentMicrobiology and Applied Sciences, 8(1), 2019, 2500–2505.
  11. [11] “Y. Hong, Y. Kim, S. Kim, H. Lee, and J. Cha, Researchtrends on environmental perception and motion planning forunmanned aerial vehicles, Electronics and TelecommunicationsTrends, 34(3), 2019, 43-54.
  12. [12] D. Kim, H. Ryu, J. Yonchorhor, and D. H. Shim, Adeep-learning-aided automatic vision-based control approachfor autonomous drone racing in game of drones com-petition, Proceedings of Machine Learning Research, 123,2020, 37–46. Accessed: May 23, 2023. [Online]. Available:https://proceedings.mlr.press/v123/kim20b.html
  13. [13] H. X. Pham, H. I. Ugurlu, J. Le Fevre, D. Bardakci, and E. Kay-acan, Deep learning for vision-based navigation in autonomousdrone racing, Deep Learning for Robot Perception andCognition, 2022, 371–406, DOI: https://doi.org/10.1016/B978-0-32-385787-1.00020-8.
  14. [14] E. Kakaletsis, C. Symeonidis, M. Tzelepi, I. Mademlis, A. Tefas,N. Nikolaidis, and I. Pitas, Computer vision for autonomousUAV flight safety: An overview and a vision-based safe landingpipeline example, ACM Computing Surveys (CSUR), 54(9),2021, DOI: https://doi.org/10.1145/3472288.
  15. [15] S. Li, M. M. O. I. Ozo, C. De Wagter, and G. C. H.E. de Croon, Autonomous drone race: A computationallyefficient vision-based navigation and control strategy, RobAuton Syst, 133, 2020, 103621, DOI: https://doi.org/10.1016/J.ROBOT.2020.103621.
  16. [16] S. Lee, D. Har, and D. Kum, Drone-assisted disastermanagement: finding victims via infrared camera and lidarsensor fusion, Proc. 3rd Asia–Pacific World Congress on Com-puter Science and Engineering, (APWC on CSE/APWCE),Nadi, 2016, 84–89, DOI: https://doi.org/ 10.1109/APWC-ON-CSE.2016.025.
  17. [17] A. Almeida, E.N. Broadbent, A.M.A. Zambrano, B.E.Wilkinson, M.E. Ferreira, R. Chazdon, P. Meli, E.B.Gorgens, C.A. Silva, S.C. Stark, R. Valbuena, D.A. Papa,and P.H.S. Brancalion, Monitoring the structure of forestrestoration plantations with a drone-lidar system, InternationalJournal of Applied Earth Observation and Geoinformation,79, 2019, 192–198, DOI: https://doi.org/10.1016/J.JAG.2019.03.014.
  18. [18] J.R. Kellner, J. Armston, M. Birrer, K.C. Cushman, L.Duncanson, C. Eck, C. Falleger, B. Imbach, K. Kr´al, M. Kr˚uˇcek,J. Trochta, T. Vrˇska and C. Zgraggen, New opportunities forforest remote sensing through ultra-high-density drone lidar,Surveys in Geophysics, 40(4), 2019, 959–977.
  19. [19] Z. Wang, Y. Wu, and Q. Niu, Multi-sensor fusion in automateddriving: A survey, IEEE Access, 8, 2020, 2847–2868.
  20. [20] D. J. Yeong, G. Velasco-Hernandez, J. Barry, and J. Walsh,Sensor and sensor fusion technology in autonomous vehicles:A review, Sensors, 21(6), 2021, 2140.
  21. [21] Y. Li and J. Ibanez-Guzman, Lidar for autonomous driving:The principles, challenges, and trends for automotive lidarand perception systems, IEEE Signal Processing Magazine,34(4), 50–61, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9127855/?casa token=Fvkl1aXNj0IAAAAA:xrog2ZB-wogu951yMXWTyH e-8GvpD5EkQH4MQ RMbdNR6WoKi2ROmzW-dg-JxG0TcNIJxM
  22. [22] D. Lv, X. Ying, Y. Cui, J. Song, K. Qian, and M. Li,Research on the technology of LIDAR data processing,Proc. 1st International Conference on Electronics Instru-mentation and Information Systems, Harbin, 2018, 1–5,DOI:https://doi.org/10.1109/EIIS.2017.8298694.
  23. [23] J. Kim, Y.S. Lee, S.S. Han, S.H. Kim, G.H. Lee,H.J. Ji, H.J. Choi, and K.N. Choi, Autonomousflight system using marker recognition on drone, Proc.Frontiers of Computer Vision, Mokpo, 2015, 1–4, DOI:https://doi.org/10.1109/FCV.2015.7103712.
  24. [24] D. Dung Nguyen, J. Rohacs, and D. Rohacs Autonomousflight trajectory control system for drones in smart citytraffic management, ISPRS International Journal of Geo-Information, 10(5), 2021, 338.
  25. [25] J. J. Lugo and A. Zell, Framework for autonomous onboardnavigation with the AR.Drone, Journal of Intelligent & RoboticSystems, 73(1), 2013, 401–412.
  26. [26] W. Gu, K.P. Valavanis, M.J. Rutherford, and A. Rizzo, UAVmodel-based flight control with artificial neural networks: Asurvey, Journal of Intelligent and Robotic Systems, 100(3–4),2020, 1469–1491.
  27. [27] S. Patel, A. Sarabakha, D. Kircali, G. Loianno, and E. Kayacan,Artificial neural network-assisted controller for fast and agileUAV flight: Onboard implementation and experimental results,Proc. Workshop on Research, Education and Development ofUnmanned Aerial Systems, 2019, 37–43. [Online]. Available:https://ieeexplore.ieee.org/abstract/document/8999677/
  28. [28] O. Khatib, Real-time obstacle avoidance for manipulators andmobile robots, International Journal of Robotics Research,5(1), 1986, 90–98. Accessed: Jun. 12, 2023. [Online]. Available:https://journals.sagepub.com/doi/abs/10.1177/027836498600500106?casa token=izJfCeHQ42gAAAAA:ydZdIZLmFq9L0LNuPwUE-M5mvYu3dUZsX1axlMd8A608ZsWeBQiCld9hrXG5rZIvm1GBz-SVujE
  29. [29] M. Thangaraj and R.S. Sangam, Intelligent UAV pathplanning framework using artificial neural network and artificialpotential field, Indonesian Journal of Electrical Engineeringand Computer Science, 29(2), 2023, 1192–1200.
  30. [30] M. Jafari and H. Xu, Intelligent control for unmanned aerialsystems with system uncertainties and disturbances usingartificial neural network, Drones, 2(3), 2018, 1–13. [Online].Available: https://www.mdpi.com/33314015
  31. [31] M.D. Buhmann, Radial basis functions, Acta Numerica, 9,2000, 1–38.
  32. [32] S. Son, Waypoint and artificial intelligence(AI) for autonomousdriving of drones, Seoul, 2019.
  33. [33] Y. Zhong, Z. Wang, A.V. Yalamanchili, A.Yadav, B.N. R.Srivatsa, S. Saripalli, and S.T.S. Bukkapatnam, Image-basedflight control of unmanned aerial vehicles (UAVs) for materialhandling in custom manufacturing, Journal of ManufacturingSystems, 56, 2020. 615–621.
  34. [34] K. Hidaka, D. Fujimoto, and K. Sato, Autonomous adaptiveflight control of a UAV for practical bridge inspection usingmultiple-camera image coupling method, 31(6), 2019, 845–854,DOI: https://doi.org/10.20965/jrm.2019.p0845.
  35. [35] L. Ma and S. Tian, A hybrid CNN-LSTM model for aircraft 4Dtrajectory prediction, IEEE Access, 8, 2020, 134668–134680.
  36. [36] W. Zeng, Z. Quan, Z. Zhao, C. Xie, and X. Lu, A deep learningapproach for aircraft trajectory prediction in terminal airspace,IEEE Access, 8, 2020, 151250–151266.
  37. [37] C. Conte, D. Accardo, and G. Rufino, Trajectory flight-time prediction based on machine learning for unmannedtraffic management, Proc. AIAA/IEEE Digital AvionicsSystems Conference, San Antonio, TX, 2020, 1–6, DOI:https://doi.org/10.1109/DASC50938.2020.9256513.
  38. [38] M. Collotta, G. Pau, and R. Caponetto,, A real-timesystem based on a neural network model to controlhexacopter trajectories, Proc. International Symposium onPower Electronics, Electrical Drives, Automation and Motion,Ischia, 2014, 222–227.
  39. [39] M. Xue, UAV trajectory modeling using neural networks, Proc.17th AIAA Aviation Technology, Integration, and OperationsConference, 2017, 1–10, DOI: https://doi.org/10.2514/6.2017-3072.
  40. [40] Z.J. Wu, S. Tian, and L. Ma, A 4D trajectory prediction modelbased on the BP neural network, Journal of Intelligent Systems,29(1), 2020, 1545–1557, DOI: https://doi.org/10.1515/JISYS-2019-0077/MACHINEREADABLECITATION/RIS.
  41. [41] T.Luukkonen, Modelling and control of quadcopter, Indepen-dent Research Project in Applied Mathematics, 22, 2011, 1–26.
  42. [42] B. Bak, R. Myung, H. Yu, and S. Choi, Predicting slope of dronewith neural network regression, Proceedings of the Korea Infor-mation Processing Society Conference, 23(2), 2016, 625–627.
  43. [43] H. Chen, K. Chang, and C.S. Agate, UAV path planningwith tangent-plus-Lyapunov vector field guidance and obstacleavoidance, IEEE Transactions on Aerospace and ElectronicSystems, 49(2), 2013, 840–856.
  44. [44] R. Aruneshwaran, S. Suresh, J. Wang, and T.K. Venugopalan,Neural adaptive flight controller for ducted-fan UAV performingnonlinear maneuver, Proc. IEEE Symposium on ComputationalIntelligence for Security and Defense Applications, Singapore,2013, 51–56.
  45. [45] Z. Xie, Y. Xia, and M. Fu, Robust trajectory-tracking methodfor UAV using nonlinear dynamic inversion, Proc. IEEE5th International Conference on Cybernetics and IntelligentSystems, Qingdao, 2011, 93–98.
  46. [46] T. M.Mitchell, Artificial neural networks, cs.cmu.edu, 2010,Accessed: Mar. 22, 2023. [Online]. Available: http://www.cs.cmu.edu/∼epxing/Class/10701-10s/Lecture/lecture7.pdf
  47. [47] ShawnWuPlus, Drone trajectory data,https://www.kaggle.com/datasets/shawnwuplus/drone-trajectory-data, 2020.
  48. [48] Z. Du, X. Jin, and Y. Yang, Fault diagnosis for temperature,flow rate and pressure sensors in VAV systems using waveletneural network, Applied Energy, 86(9), 2009, 1624–1631.
  49. [49] K. Wang, X. Qi, and H. Liu, A comparison of day-aheadphotovoltaic power forecasting models based on deep learningneural network, Applied Energy, 251, 2019, 113315.
  50. [50] H. Krsti´c, ˇZ. Koˇski, I. Otkovi´c, and M. ˇSpani´c, Application ofneural networks in predicting airtightness of residential units,Energy and Buildings, 84, 2014, 160–168.
  51. [51] K. Viswanathan, A. Krishnakumari, and D. Dinakaran,Prediction model for wheel loading in grinding usingvibration analysis and ANN, International Journal ofRobotics and Automation, 36(6), 2021, 59–66, DOI:https://doi.org/10.2316/J.2021.206-0439.
  52. [52] R. Jovanovi´c, A. Sretenovi´c, and B.D. ˇZ ivkovi´c, Ensembleof various neural networks for prediction of heating energyconsumption, Energy and Buildings, 94, 2015, 189–199.
  53. [53] S. Mohanty, P.K. Patra, and S.S. Sahoo, Prediction of globalsolar radiation using nonlinear auto regressive network withexogenous inputs (NARX), Proc. 39th National SystemsConference, Greater Noida, 2015, 1–6.
  54. [54] Q. Cao, B. Ewing, and M.A. Thompson, Forecasting windspeed with recurrent neural networks, European Journal ofOperational Research, 221(1), 2012, 148–154.
  55. [55] O. Omolaye, G. Igwue, and G.A.Akpakwu, Okumura-Hata: Aperfect model for driving route UHF investigation, AmericanJournal of Engineering Research, 4(9), 2015, 139–147.
  56. [56] L. Ljung and T. Glad, Modeling of dynamic systems,Englewood Cliffs, NJ, USA: Prentice Hall,1994, Accessed:May 18, 2022. [Online]. Available: https://dl.acm.org/doi/abs/10.5555/197225
  57. [57] O. Philip, B.A.-Adeleke Predictive and comparative analysisof NARX and NIO time series prediction, American Journalof Engineering Research, 6, 2017, 155–165.
  58. [58] J.M. Caswell, A nonlinear autoregressive approach to statisticalprediction of disturbance storm time geomagnetic fluctuationsusing solar data, Journal of Signal and Information Processing,5(2), 2014, 42–53.
  59. [59] F. Burden and D. Winkler, Bayesian regularization of neuralnetworks, Methods in Molecular Biology, 458, 2008, 25–44.
  60. [60] P.B. deHarrington, Sigmoid transfer functions in backpropa-gation neural networks, Analytical Chemistry, 65(15), 1993,2167–2168.

Important Links:

Go Back