Abstract—Unmanned Aerial Vehicles (UAVs) rely on navigation commands from autonomous flight control system or from Ground Control System (GCS) via line-of-sight wireless data link. UAV needs to perform immediate landing on predefined airfield in case of extreme emergency like navigation, data link, engine or control surface failure, that cannot be accomplished in some cases and accidents can occur which can result collateral damage as well. This paper presents the system design which can discover the appropriate area within the surroundings for immediate landing in case of emergency. The proposed system design consists of two stages. During first stage, system takes top view images from UAV onboard camera, then image processing algorithm extracts and refine the attributes of the image. In second stage, machine learning based algorithm evaluates the results from previous stage, and based on its previous training, decides whether the area visible in image is good for safe landing or not. We implement proposed system design in MATLAB and the approach used is validated with experimental results on test data. Proposed system design uses combination of simple techniques, which makes it less computationally intensive, having reduced latency, low implementation cost and easy to implement on high speed real time hardware like FPGA/ASIC.
Index Terms—vision based landing, emergency landing, machine vision, machine learning
Cite: Sumair Aziz, Rao Muhammad Faheem, Mudassar Bashir, Adnan Khalid, and Amanullah Yasin, "Unmanned Aerial Vehicle Emergency Landing Site Identification System Using Machine Vision," Journal of Image and Graphics, Vol. 4, No. 1, pp. 36-41, June 2016. doi: 10.18178/joig.4.1.36-41
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