147 research outputs found

    Improved situation awareness for autonomous taxiing through self-learning

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    As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs’ autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. Indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically

    Systems Engineering Design of an Electronically Interactive Application for Runway Incursion Prevention

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    Runway Incursion is the leading cause of serious incidents or accidents in airports. One of the most common causes of a runway incursion is airport unfamiliarity. Therefore, the researcher designed an electronically interactive application as a practice tool for pilots to utilize during flight preparation. The objective of this application is to enhance airport familiarity to ultimately reduce runway incursion. This application is interactive, affordable, accessible, and mobile device-based. It was designed using the Systems Engineering approach, following Human Factors Engineering principles to make this application user-friendly and to provide optimized human machine interaction. A model-based Systems Engineering software-CORE was utilized to manage the system requirements and provide clear traceability and rationality for each function. A prototype of the interface was developed and evaluated using a heuristic evaluation approach. The experts participating in the evaluation generally agreed that this application would provide an enhanced learning experience of the airport environment during flight preparation rather than studying the FAA airport diagram alone. This project provides a guideline for Software engineers to program this application expeditiously with the least amount of confusion

    Multi-modal cockpit interface for improved airport surface operations

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    A system for multi-modal cockpit interface during surface operation of an aircraft comprises a head tracking device, a processing element, and a full-color head worn display. The processing element is configured to receive head position information from the head tracking device, to receive current location information of the aircraft, and to render a virtual airport scene corresponding to the head position information and the current aircraft location. The full-color head worn display is configured to receive the virtual airport scene from the processing element and to display the virtual airport scene. The current location information may be received from one of a global positioning system or an inertial navigation system

    Automated taxiing for unmanned aircraft systems

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    Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation

    Colour based semantic image segmentation and classification for unmanned ground operations

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    To aid an automatic taxiing system for unmanned aircraft, this paper presents a colour based method for semantic segmentation and image classification in an aerodrome environment with the intention to use the classification output to aid navigation and collision avoidance. Based on previous work, this machine vision system uses semantic segmentation to interpret the scene. Following an initial superpixel based segmentation procedure, a colour based Bayesian Network classifier is trained and used to semantically classify each segmented cluster. HSV colourspace is adopted as it is close to the way of human vision perception of the world, and each channel shows significant differentiation between classes. Luminance is used to identify surface lines on the taxiway, which is then fused with colour classification to give improved classification results. The classification performance of the proposed colour based classifier is tested in a real aerodrome, which demonstrates that the proposed method outperforms a previously developed texture only based method

    Automatic Update of Airport GIS by Remote Sensing Image Analysis

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    This project investigates ways to automatically update Geographic Information Systems (GIS) for airports by analysis of Very High Resolution (VHR) remote sensing images. These GIS databases map the physical layout of an airport by representing a broad range of features (such as runways, taxiways and roads) as georeferenced vector objects. Updating such systems therefore involves both automatic detection of relevant objects from remotely sensed images, and comparison of these objects between bi-temporal images. The size of the VHR images and the diversity of the object types to be captured in the GIS databases makes this a very large and complex problem. Therefore we must split it into smaller parts which can be framed as instances of image processing problems. The aim of this project is to apply a range of methodologies to these problems and compare their results, providing quantitative data where possible. In this report, we devote a chapter to each sub-problem that was focussed on. Chapter 1 begins by introducing the background and motivation of the project, and describes the problem in more detail. Chapter 2 presents a method for detecting and segmenting runways, by detecting their distinctive markings and feeding them into a modified Hough transform. The algorithm was tested on a dataset of six bi-temporal remote sensing image pairs and validated against manually generated ground-truth GIS data, provided by Jeppesen. Chapter 3 investigates co-registration of bi-temporal images, as a necessary precursor to most direct change detection algorithms. Chapter 4 then tests a range of bi-temporal change detection algorithms (some standard, some novel) on co-registered images of airports, with the aim of producing a change heat-map which may assist a human operator in rapidly focussing attention on areas that have changed significantly. Chapter 5 explores a number of approaches to detecting curvilinear AMDB features such as taxilines and stopbars, by means of enhancing such features and suppressing others, prior to thresholding. Finally in Chapter 6 we develop a method for distinguishing between AMDB lines and other curvilinear structures that may occur in an image, by analysing the connectivity between such features and the runways

    Machine vision for UAS ground operations: using semantic segmentation with a bayesian network classifier

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    This paper discusses the machine vision element of a system designed to allow Unmanned Aerial System (UAS) to perform automated taxiing around civil aerodromes, with only a monocular camera. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detecting potential collision risks. In practice, untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are used to estimate the class. As the competency of each individual estimate can vary dependent on multiple factors (number of pixels, lighting conditions and even surface type). A Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for localisation and obstacle detection

    Map-enhanced visual taxiway extraction for autonomous taxiing of UAVs

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    In this paper, a map-enhanced method is proposed for vision-based taxiway centreline extraction, which is a prerequisite of autonomous visual navigation systems for unmanned aerial vehicles. Comparing with other sensors, cameras are able to provide richer information. Consequently, vision based navigations have been intensively studied in the recent two decades and computer vision techniques are shown to be capable of dealing with various problems in applications. However, there are signi cant drawbacks associated with these computer vision techniques that the accuracy and robustness may not meet the required standard in some application scenarios. In this paper, a taxiway map is incorporated into the analysis as prior knowledge to improve on the vehicle localisation and vision based centreline extraction. We develop a map updating algorithm so that the traditional map is able to adapt to the dynamic environment via Bayesian learning. The developed method is illustrated using a simulation study

    Non-airborne conflicts: The causes and effects of runway transgressions

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    The 1210 ASRS runway transgression reports are studied and expanded to yield descriptive statistics. Additionally, a one of three subset was studied in detail for purposes of evaluating the causes, risks, and consequences behind trangression events. Occurrences are subdivided by enabling factor and flight phase designations. It is concluded that a larger risk of collision is associated with controller enabled departure transgressions over all other categories. The influence of this type is especially evident during the period following the air traffic controllers' strike of 1981. Causal analysis indicates that, coincidentally, controller enabled departure transgressions also, show the strongest correlations between causal factors. It shows that departure errors occur more often when visibility is reduced, and when multiple takeoff runways or intersection takeoffs are employed. In general, runway transgressions attributable to both pilot and controller errors arise from three problem areas: information transfer, awareness, and spatial judgement. Enhanced awareness by controllers will probably reduce controller enabled incidents

    High speed research system study. Advanced flight deck configuration effects

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    In mid-1991 NASA contracted with industry to study the high-speed civil transport (HSCT) flight deck challenges and assess the benefits, prior to initiating their High Speed Research Program (HSRP) Phase 2 efforts, then scheduled for FY-93. The results of this nine-month effort are presented, and a number of the most significant findings for the specified advanced concepts are highlighted: (1) a no nose-droop configuration; (2) a far forward cockpit location; and (3) advanced crew monitoring and control of complex systems. The results indicate that the no nose-droop configuration is critically dependent upon the design and development of a safe, reliable, and certifiable Synthetic Vision System (SVS). The droop-nose configuration would cause significant weight, performance, and cost penalties. The far forward cockpit location, with the conventional side-by-side seating provides little economic advantage; however, a configuration with a tandem seating arrangement provides a substantial increase in either additional payload (i.e., passengers) or potential downsizing of the vehicle with resulting increases in performance efficiencies and associated reductions in emissions. Without a droop nose, forward external visibility is negated and takeoff/landing guidance and control must rely on the use of the SVS. The technologies enabling such capabilities, which de facto provides for Category 3 all-weather operations on every flight independent of weather, represent a dramatic benefits multiplier in a 2005 global ATM network: both in terms of enhanced economic viability and environmental acceptability
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