EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 417 (2025) MATEC Web Conf., 417 (2025) 10003 References
Open Access
Issue
MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
Article Number 10003
Number of page(s) 15
Section Pattern Recognition
DOI https://doi.org/10.1051/matecconf/202541710003
Published online 25 November 2025
  1. Western Cape Government, Estuaries in South Africa (Western Cape Department of Environmental Affairs & Development Planning, Cape Town, 2017). Available: https://www.westerncape.gov.za/eadp/sites/eadp.westerncape.gov.za/files/atoms/files/E stuaries%20of%20the%20WC%20-%20DL%20Brochure.pdf (accessed 10 Apr 2024). [Google Scholar]
  2. Z. Chen, M. Bowen, G. Li, G. Coco, B. Hall, Retention and dispersion of buoyant plastic debris in a well-mixed estuary from drifter observations. Mar. Pollut. Bull. 180, 113793 (2022). https://doi.org/10.1016/j.marpolbul.2022.113793 [Google Scholar]
  3. M. Gästgifvars, H. Lauri, A. Sarkanen, K. Myrberg, O. Andrejev, C. Ambjörn, Modelling surface drifting of buoys during a rapidly-moving weather front in the Gulf of Finland, Baltic Sea. Estuar. Coast. Shelf Sci. 70, 567–576 (2006). https://doi.org/10.1016/j.ecss.2006.06.030 [Google Scholar]
  4. Q. Liang, Z. Wang, Y. Yin, W. Xiong, J. Zhang, Z. Yang, Autonomous aerial obstacle avoidance using LiDAR sensor fusion. PLoS ONE 18, e0287177 (2023). https://doi.org/10.1371/journal.pone.0287177 [Google Scholar]
  5. A. C. B. Chiella, H. N. Machado, B. O. S. Teixeira, G. A. S. Pereira, GNSS/LiDAR-based navigation of an aerial robot in sparse forests. Sensors 19, 4061 (2019). https://doi.org/10.3390/s19184061 [Google Scholar]
  6. M. Y. Arafat, M. M. Alam, S. Moh, Vision-based navigation techniques for unmanned aerial vehicles. Drones 7, 89 (2023). https://doi.org/10.3390/drones7020089 [CrossRef] [Google Scholar]
  7. J. Xiao, R. Zhang, Y. Zhang, M. Feroskhan, Vision-based learning for drones: a survey. *arXiv:*2312.05019 v2 [cs.RO] (2024). https://doi.org/10.48550/arXiv.231205019 [Google Scholar]
  8. V. R. F. Miranda, A. M. C. Rezende, T. L. Rocha, H. Azpúrua, L. C. A. Pimenta, G. M. Freitas, Autonomous navigation system for a delivery drone. J. Control Autom. Electr. Syst. 33, 141–155 (2022). https://doi.org/10.1007/s40313-021-00828-4 [Google Scholar]
  9. M. Kalaitzakis, B. Cain, S. Carroll, A. Ambrosi, C. Whitehead, N. Vitzilaios, Fiducial markers for pose estimation: overview, applications and experimental comparison of the ARTag, AprilTag, ArUco and STag markers. J. Intell. Robot. Syst. 101, 71 (2021). https://doi.org/10.1007/s10846-020-01307-9 [Google Scholar]
  10. H. Jiaxin, G. Yanning, F. Zhen, G. Yuqing, Vision-based autonomous landing of unmanned aerial vehicles, in Proc. 2017 Chinese Automation Congress (CAC) (Jinan, China, 20–22 Oct 2017), pp. 3464–3469. https://doi.org/10.1109/CAC.2017.8243380 [Google Scholar]
  11. S. Hao, G. Song, Y. Gu, J. Mao, Z. Ji, S. Xie, A. Song, Vision-based autonomous detecting and grasping framework for the fully-actuated aerial manipulator, in Proc. 2023 IEEE Int. Conf. Robot. Biomimetics (ROBIO) (2023), pp. 1–6. https://doi.org/10.1109/ROBIO58561.2023.10354730 [Google Scholar]
  12. H.-T. Hsiao, J. Sun, H. Zhang, J. Zhao, A mechanically intelligent and passive gripper for aerial perching and grasping. IEEE/ASME Trans. Mechatronics 27, 5243–5253 (2022). https://doi.org/10.1109/TMECH.2022.3175649 [Google Scholar]
  13. H. Zhang, J. Sun, J. Zhao, Compliant bistable gripper for aerial perching and grasping, in Proc. 2019 IEEE Int. Conf. Robot. Autom. (ICRA), Montréal (2019), pp. 1027–1032. https://doi.org/10.1109/ICRA.2019.8793936 [Google Scholar]
  14. U. A. Fiaz, M. Abdelkader, J. S. Shamma, An intelligent gripper design for autonomous aerial transport with passive magnetic grasping and dual-impulsive release, in Proc. 2018 IEEE/ASME Adv. Intell. Mechatronics (AIM), Auckland (2018), pp. 1027–1032. https://doi.org/10.1109/AIM.2018.8452383 [Google Scholar]
  15. I. Hussain, M. Malvezzi, D. Gan, Z. Iqbal, L. Seneviratne, D. Prattichizzo, F. Renda, Compliant gripper design, prototyping and modeling using screw theory formulation. Int. J. Robot. Res. 40, 55–71 (2021). https://doi.org/10.1177/0278364920947818 [Google Scholar]
  16. H. Kwon, J. Yoder, S. Baek, S. Gruber, D. Pack, Maximizing water surface target localization accuracy under sunlight reflection with an autonomous unmanned aerial vehicle. J. Intell. Robot. Syst. 74, 395–411 (2014). https://doi.org/10.1007/s10846-013-9944-1 [Google Scholar]
  17. O. T. Arnegaard, F. S. Leira, H. H. Helgesen, S. Kemna, T. A. Johansen, Detection of objects on the ocean surface from a UAV with visual and thermal cameras: a machine learning approach, in Proc. 2021 Int. Conf. Unmanned Aircraft Syst. (ICUAS), Athens (2021), pp. 639–646. [Google Scholar]
  18. M. J. Cho, C. Han, Estimation of hovering flight time of battery-powered multicopters. J. Aerosp. Syst. Eng. 15, 11–20 (2021). https://doi.org/10.20910/JASE.2021.15.4.11 [Google Scholar]
  19. M. I. A. Lourakis, A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar, Tech. Rep., Inst. Comput. Sci., Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece (2005). [Online]. Available: https://www.researchgate.net/publication/239328019_A_Brief_Description_of_the_Le venberg-Marquardt_Algorithm_Implemened_by_levmar [Google Scholar]
  20. G. J. Fouché, R. Malekian, Drone as an autonomous aerial sensor system for motion planning. Meas. 119, 142–155 (2018). https://doi.org/10.1016/j.measurement.2018.01.027 [Google Scholar]
  21. L. Lin, Y. Yang, H. Cheng, X. Chen, Autonomous vision-based aerial grasping for rotorcraft unmanned aerial vehicles. Sensors 19, 3410 (2019). https://doi.org/10.3390/s19153410 [Google Scholar]
  22. S. Lin, J. Wang, R. Peng, W. Yang, Development of an autonomous unmanned aerial manipulator based on a real-time oriented-object detection method. Sensors 19, 2396 (2019). https://doi.org/10.3390/s19102396 [Google Scholar]
  23. A. Matus-Vargas, G. Rodriguez-Gomez, J. Martinez-Carranza, Ground effect on rotorcraft unmanned aerial vehicles: a review. Intell. Serv. Robot. 14, 99–118 (2021). https://doi.org/10.1007/s11370-020-00344-5 [Google Scholar]
  24. Ocean Observers, “Drifting buoys – DBCP,” 31 Oct 2023. Available: https://www.oceanobservers.org/Learn-about-observing-the-ocean/Drifting-buoys-DBCP (accessed 3 Nov 2024). [Google Scholar]
  25. E. Bauer, B. G. Cangan, R. K. Katzschmann, Autonomous marker-less rapid aerial grasping, in Proc. 2023 IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Detroit (2023), pp. 6395–6402. https://doi.org/10.48550/arXiv.2211.13093 [Google Scholar]
  26. S. Jones, “NOAA’s array of drifting ocean buoys,” Atlantic Oceanographic & Meteorological Laboratory, 6 Aug 2014. Available: https://www.aoml.noaa.gov/noaas-array-of-drifting-ocean-buoys/ (accessed 3 Nov 2024). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.