Daniel L. Fitzgerald

A Vision Based Forced Landing Site Selection System for an Autonomous UAV

This paper presents a system overview of the UAV forced landing site selection system and the results to date. The forced landing problem is a new field of research for UAVs and this paper will show the machine vision approach taken to address this problem. The results are based on aerial imagery collected from a series of flight trials in a Cessna 172. The aim of this research is to locate candidate landing sites for UAV forced landings, from aerial imagery. Output image frames highlight the algorithms selected safe landing locations. The algorithms for the problem use image processing techniques and neural networks for the classification problem. The system is capable of locating areas that are large enough to land in and that are free of obstacles 92.3% ± 2% (95% confidence) of the time. These areas identified are then further classified as to their surface type to a classification accuracy of 90% ± 3% (98% confidence). It should be noted that although the system is being designed primarily for the forced landing problem for UAVs, the research can also be applied to forced landings or glider applications for piloted aircraft.

Forced Landing Technologies for Unmanned Aerial Vehicles: Towards Safer Operations

While using unmanned systems in combat is not new, what will be new in the foreseeable future is how such systems are used and integrated in the civilian space. The potential use of Unmanned Aerial Vehicles in civil and commercial applications is becoming a fact, and is receiving considerable attention by industry and the research community. The majority of Unmanned Aerial Vehicles performing civilian tasks are restricted to flying only in segregated space, and not within the National Airspace. The areas that UAVs are restricted to flying in are typically not above populated areas, which in turn are the areas most useful for civilian applications. The reasoning behind the current restrictions is mainly due to the fact that current UAV technologies are not able to demonstrate an Equivalent Level of Safety to manned aircraft, particularly in the case of an engine failure which would require an emergency or forced landing. This chapter will preset and guide the reader through a number of developments that would facilitate the integration of UAVs into the National Airspace. Algorithms for UAV Sense-and-Avoid and Force Landings are recognized as two major enabling technologies that will allow the integration of UAVs in the civilian airspace. The following sections will describe some of the techniques that are currently being tested at the Australian Research Centre for Aerospace Automation (ARCAA), which places emphasis on the detection of candidate landing sites using computer vision, the planning of the descent path trajectory for the UAV, and the decision making process behind the selection of the final landing site.

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate.