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Navigation for drones in GPS-Denied environments based on Vision processing

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a method for the navigation of drones in GPS-denied Environments based on vision processing. A camera is attached to the drone to fly in full conditions like GPS, video streaming, control waypoint, etc. Then, some information such as the image features and location of the drone is extracted and stored, which is used for the next flight with GPS-denied Environments. The results are shown that the drone positions could estimate with high accuracy.
Słowa kluczowe
Rocznik
Tom
Strony
25--27
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Faculty of Electrical and Electronic Institute of Military science and Technology Hanoi, Vietnam
  • International School, Vietnam National University Hanoi, Vietnam
autor
  • General education University of Labour and Social Affairs Hanoi, Vietnam
  • International School, Vietnam National University Hanoi, Vietnam
Bibliografia
  • 1. A. Konert and T. Balcerzak, "Military autonomous drones (UAVs) - from fantasy to reality. Legal and Ethical implications," Transportation Research Procedia, vol. 59, pp. 292-299, 2021/01/01/ 2021.
  • 2. M. Yaqot and B. C. Menezes, "Unmanned Aerial Vehicle (UAV) in Precision Agriculture: Business Information Technology Towards Farming as a Service," in 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2021, pp. 1-7.
  • 3. A. Gupta, T. Afrin, E. Scully, and N. Yodo, "Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities," Future Transportation, vol. 1, no. 2, pp. 326-350. http://dx.doi.org/10.3390/futuretransp1020019
  • 4. A. Kurnyta, W. Zielinski, P. Reymer, K. Dragan, and M. Dziendzikowski, "Numerical and Experimental UAV Structure Investigation by Pre-Flight Load Test," (in eng), Sensors (Basel), vol. 20, no. 11, May 26 2020.
  • 5. T. S. Rachmawati and S. Kim, "Unmanned Aerial Vehicles (UAV) Integration with Digital Technologies toward Construction 4.0: A Systematic Literature Review," Sustainability, vol. 14, no. 9. http://dx.doi.org/10.3390/su14095708
  • 6. M. Stanković, M. M. Mirza, and U. Karabiyik, "UAV Forensics: DJI Mini 2 Case Study," Drones, vol. 5, no. 2. http://dx.doi.org/10.3390/drones5020049
  • 7. N. Fariñas-Álvarez, F. Navarro-Medina, and H. González-Jorge, "Metrological Validation of Pixhawk Autopilot Magnetometers in Helmholtz Cage," World Electric Vehicle Journal, vol. 13, no. 5. http://dx.doi.org/10.3390/wevj13050085
  • 8. J. Moore, A. Fein, and W. Setzler, "Design and Analysis of a Fixed-Wing Unmanned Aerial-Aquatic Vehicle," in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 1236-1243.
  • 9. M. Idrissi, M. Salami, and F. Annaz, "A Review of Quadrotor Unmanned Aerial Vehicles: Applications, Architectural Design and Control Algorithms," Journal of Intelligent & Robotic Systems, vol. 104, no. 2, p. 22, 2022/01/22 2022.
  • 10. N. Sethi and S. Ahlawat, "Low-fidelity design optimization and development of a VTOL swarm UAV with an open-source framework," Array, vol. 14, p. 100183, 2022/07/01/ 2022.
  • 11. K. D. Nguyen, C. Ha, and J. T. Jang, "Development of a New Hybrid Drone and Software-in-the-Loop Simulation Using PX4 Code," in Intelligent Computing Theories and Application, Cham, 2018, pp. 84-93: Springer International Publishing.
  • 12. A. Sheibani and M. A. Pourmina, "Simulation and Analysis of the Stability of a PID Controller for Operation of Unmanned Aerial Vehicles," in Mechanical Engineering and Technology, Berlin, Heidelberg, 2012, pp. 757-765: Springer Berlin Heidelberg.
  • 13. F. A. A. Andrade et al., "Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains," Machines, vol. 10, no. 1. http://dx.doi.org/10.3390/machines10010012
  • 14. R. P. Padhy, S. Verma, S. Ahmad, S. K. Choudhury, and P. K. Sa, "Deep Neural Network for Autonomous UAV Navigation in Indoor Corridor Environments," Procedia Computer Science, vol. 133, pp. 643-650, 2018/01/01/ 2018.
  • 15. M. Ö. Efe, "Sliding Mode Control for Unmanned Aerial Vehicles Research," in Recent Advances in Sliding Modes: From Control to Intelligent Mechatronics, X. Yu and M. Önder Efe, Eds. Cham: Springer International Publishing, 2015, pp. 239-255.
  • 16. K. D. Nguyen and T. T. Nguyen, "Vision-Based Software-in-the-Loop-Simulation for Unmanned Aerial Vehicles Using Gazebo and PX4 Open Source," in 2019 International Conference on System Science and Engineering (ICSSE), 2019, pp. 429-432.
  • 17. V. Parmar, N. Bhatia, S. Negi, and M. Suri, "Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones," CoRR, vol. abs/2007.02839, 2020 2020.
  • 18. F. Guo, J. Yang, Y. Chen, and B. Yao, "Research on image detection and matching based on SIFT features," in 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), 2018, pp. 130-134.
  • 19. L. Tang, S. Ma, X. Ma, and H. You, "Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification," Applied Sciences, vol. 12, no. 17. http://dx.doi.org/10.3390/app12178448
Uwagi
PL
Opracowane ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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