An Automatic Zone Detection System for Safe Landing of UAVs

As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems

A new Measure for Optimization of Field Sensor Network with Application to LiDAR

This thesis proposes a solution to the problem of modeling and optimizing the field sensor network in terms of the coverage performance. The term field sensor is referred to a class of sensors which can detect the regions in 2D/3D spaces through non-contact measurements. The most widely used field sensors include cameras, LiDAR, ultrasonic sensor, and RADAR, etc. The key challenge in the applications of field sensor networks, such as area coverage, is to develop an effective performance measure, which has to involve both sensor and environment parameters. The nature of space distribution in the case of the field sensor incurs a great deal of difficulties for such development and, hence, poses it as a very interesting research problem. Therefore, to tackle this problem, several attempts have been made in the literature. However, they have failed to address a comprehensive and applicable approach to distinctive types of field sensors (in 3D), as only coverage of a particular sensor is usually addressed at the time. In addition, no coverage model has been proposed yet for some types of field sensors such as LiDAR sensors. In this dissertation, a coverage model is obtained for the field sensors based on the transformation of sensor and task parameters into the sensor geometric model. By providing a mathematical description of the sensor’s sensing region, a performance measure is introduced which characterizes the closeness between a single sensor and target configurations. In this regard, the first contribution is developing an Infinity norm based measure which describes the target distance to the closure of the sensing region expressed by an area-based approach. The second contribution can be geometrically interpreted as mapping the sensor’s sensing region to an n-ball using a homeomorphism map and developing a performance measure. The third contribution is introducing the measurement principle and establishing the coverage model for the class of solid-state (flash) LiDAR sensors. The fourth contribution is point density analysis and developing the coverage model for the class of mechanical (prism rotating mechanism) LiDAR sensors. Finally, the effectiveness of the proposed coverage model is illustrated by simulations, experiments, and comparisons is carried out throughout the dissertation. This coverage model is a powerful tool as it applies to the variety of field sensors

Safe Landing Site Detection Using SRTM Data for the Unmanned Aerial Vehicles

Bu çalışmada, İnsansız Hava Araçları (İHA) için, görev icrası esnasında acil iniş gerektiren durumlarda, uygun iniş bölgesinin belirlenmesi amaçlanmıştır. Bu amaç doğrultusunda da SRTM (Shuttle Radar Topography Mission) haritalarından yararlanılmıştır. Günümüzde İnsansız Hava Araçlarının askeri ve sivil amaçlı olarak yoğun bir şekilde kullanıldığı görülmektedir. Artık İHA‘ların otonomisinin artırılması ve insana olan ihtiyacın daha aza indirgenmesi İHA sistemlerinin daha akıllı hale getirilmesi kaçınılmaz bir ihtiyaç haline gelmiştir. İHA‘ların görev icrası anında beklenmedik problemler ile karşılaşılmaktadır. (motor arızası, haberleşmenin kesilmesi vs.) Bu gibi durumlarda İHA’nın acil iniş sistemini devreye alıp otonom olarak bu alana inişini gerçekleştirmesi gerekmektedir. SRTM yükseklik haritası kullanılarak olası iniş alanlarının tespit edilmesinde görüntü bölütleme yöntemi ve blob analizi kullanılmıştır. Tasarlanan sistem ile İHA‘nın acil durumlarda istenmeyen bir bölgeye zorunlu inişi kısıtlanmakta ve İHA belirlenen güvenli bir alana yönlendirilmektedir.In this work, it is aimed to determine the suitable landing zones for the Unmanned Aerial Vehicles (UAVs) in case of an emergency during their missions. SRTM (Shuttle Radar Topography Mission) maps were used to reach that aim. Nowadays, it is observed that the UAVs are being used densely for both military and civilian purposes. So, it is inevitable to make the UAVs smarter and make them more autonomous to minimize their dependence on a person. UAVs can have unexpected problems during their missions such as motor fault, communication cut etc. In this situation, a UAV should activate the emergency landing systems and realize the landing safely. Image segmentation and blob analysis are used to determine the possible landing zones on the SRTM data. In this proposed system, the landing of a UAV to unwanted zones is limited and the UAV is guidance to the predetermined safe zones