Autonomous UAV forced landing site prediction using machine learning

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.