Autonomous UAV forced landing site prediction using machine learning

Gracelin Hepsiba J 1, Purnima RG 1, Shinthu Bargavi V 1, Sakthi Kiran MB 1, Merlin Beaulah J 2 and MS Vinotheni 1

1 Department of Electronics engineering, Student, MIT Campus, Chromepet, Anna University, Chennai, India.
2 Department of Computer Science and Business System, Panimalar Engineering College, Chennai, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(01), 1010–1016.
Article DOI: 10.30574/ijsra.2024.12.1.0894
Publication history: 
Received on 16 April 2024 revised on 24 May 2024; accepted on 26 May 2024
 
Abstract: 
Unmanned aerial vehicles, or UAVs, are being used in an increasing range of applications, including surveillance, search and rescue, and environmental tracking. However, unanticipated engine issues, engine failures, and breakdown of the flying surface may necessitate forced landings, putting the UAV and its surroundings in danger. If there are any obstacles in the way of the UAV's ability to land safely, such as buildings or trees, it must be able to return to its emergency landing place. Thus, in these emergency scenarios, automated technology that can identify safe landing places rapidly. This paper presents an innovative approach that adds feature extraction, including HOG, HSV, LBP, and SFIT. GMM, SVM and kernels that use machine learning techniques to instinctively select the proper UAV-forced landing places. Through the use of machine learning and feature extraction techniques, we raised our accuracy by 40% over the baseline. The proposed system integrates data from several sources, including topography maps, satellite images, and board sensors. The machine learning algorithms predict possible landing sites. Annotated datasets with factors including topographic height, land cover type, slope, and proximity to obstacles are used to train these algorithms. especially artificial neural networks, or ANNs.
 
Keywords: 
Machine Learning; Detecting Safe Zone; Automated Landing; Gaussian Mixture Model; Support Vector Model.
 
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