Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks

We introduce the Intelligent Autopilot System (IAS) which is capable of autonomous landing, and go-around of large jets such as airliners under severe weather conditions. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to autonomously handle flight uncertainties such as severe weather conditions, autonomous complete flights, and go-around. A robust approach to control the aircraft's bearing using Artificial Neural Networks is proposed. An Artificial Neural Network predicts the appropriate bearing to be followed given the drift from the path line to be intercepted. In addition, the capabilities of the Flight Manager of the IAS are extended to detect unsafe landing attempts, and generate a go-around flight course. Experiments show that the IAS can handle such flight skills and tasks effectively, and can even land aircraft under severe weather conditions that are beyond the maximum demonstrated landing of the aircraft model used in this work as reported by the manufacturer's operations limitations. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Sensing and Estimation of Airflow Angles and Atmospheric Winds for Small Unmanned Aerial Vehicles

This dissertation focuses on development of new sensing, estimation, and analysis methods for unmanned aerial vehicle (UAV) operations in dynamic wind fields. Three main problems are studied, including airflow angle estimation, 3D wind estimation, and UAV wake encounter identification, simulation, and validation. A thorough survey is performed first on wind sensing and estimation methods using fixed-wing UAVs. Four flow angle estimation filters are then proposed and validated for accurate UAV flow angle estimation at low cost. Furthermore, two 3D wind estimation filters are proposed for small fixed-wing UAVs and validated by utilizing different wind models. Finally, a novel UAV wake encounter simulation platform is developed to simulate UAV response during wake encounters and compared with results from close formation wake encounter flight

Flight Characteristic Verification of the Variable Camber Compliant Wing

Morphing wing technology gives aircraft the ability to change wing shape to control the aircraft and flight performance characteristics. AFIT, AFRL and USU Aero Lab have collaborated to design and test a variable camber compliant wing (VCCW) on a small unmanned aerial vehicle (UAV). Flight tests demonstrated the wing performance and provided data to refine a VCCW flight simulator. Work was completed with the USU AeroLab-generated MachUp and the actual flight data to improve the simulator to provide results close to those of the actual flight test. The research provides a tool to reduce time and cost for future flight testing for VCCW development

Photomosaicing and automatic topography generation from stereo aerial photography

Master of ScienceDepartment of Mechanical and Nuclear EngineeringDale E. Schinstock, Chris LewisThe Autonomous Vehicle Systems Lab specializes in using autonomous planes for remote sensing applications. By developing an inexpensive image acquisition platform and the algorithms to post process the data, remote sensing can be performed at a lower monetary cost with shorter lead times. This thesis presents one algorithm that has shown to be an effective alternative to the traditional Bundle Adjustment (BA) algorithm used for making composite images from many individual overlapping images. BA simultaneously estimates camera poses and visible feature locations from blocks of overlapping imagery, but is computationally expensive. The alternate algorithm (ABA) uses a cost function that does not explicitly include the feature locations. For photographic sets covering large areas, but having overlap only between adjacent photos, the search space and consequently the computational cost is significantly reduced when compared to typical BA. The usefulness of the algorithm is demonstrated by comparing a digital elevation model created through the ABA with LIDAR data

Relative Timing of Off-Axis Volcanism from Sediment Thickness Estimates on the 8°20’N Seamount Chain, East Pacific Rise

Volcanic seamount chains on the flanks of mid-ocean ridges record variability in magmatic processes associated with mantle melting over several millions of years. However, the relative timing of magmatism on individual seamounts along a chain can be difficult to estimate without in situ sampling and is further hampered by Ar40/Ar39 dating limitations. The 8°20’N seamount chain extends ∼170 km west from the fast-spreading East Pacific Rise (EPR), north of and parallel to the western Siqueiros fracture zone. Here, we use multibeam bathymetric data to investigate relationships between abyssal hill formation and seamount volcanism, transform fault slip, and tectonic rotation. Near-bottom compressed high-intensity radiated pulse, bathymetric, and sidescan sonar data collected with the autonomous underwater vehicle Sentry are used to test the hypothesis that seamount volcanism is age-progressive along the seamount chain. Although sediment on seamount flanks is likely to be reworked by gravitational mass-wasting and current activity, bathymetric relief and Sentry vehicle heading analysis suggest that sedimentary accumulations on seamount summits are likely to be relatively pristine. Sediment thickness on the seamounts\u27 summits does not increase linearly with nominal crustal age, as would be predicted if seamounts were constructed proximal to the EPR axis and then aged as the lithosphere cooled and subsided away from the ridge. The thickest sediments are found at the center of the chain, implying the most ancient volcanism there, rather than on seamounts furthest from the EPR. The nonlinear sediment thickness along the 8°20’N seamounts suggests that volcanism can persist off-axis for several million years

Detecting medusahead (Taeniatherum caput-medusae (L.) Nevski) using high frequency, sequential, globally positioned digital images

Invasive plant species are expanding and transforming vegetative communities across Oregon and throughout the United States. Over the past three decades remote sensing, geographic information system (GIS), and Global Positioning System (GPS) technologies have been integrated to detect and map the distribution of noxious rangeland plants. This study developed low-cost protocols to detect and map Medusahead (Taeniatherum caput-medusae (L.) Nevski) weed infestations using GPS loggers to track aircraft/camera position, altitude, and bearing, as well as Aerial Image Positioning Tool software to geographically rectify and project each aerial image. We then mapped the extent of medusahead in target areas and evaluated patterns of infestation. Flying in a single engine fixed-wing aircraft, images were collected every five seconds, with a total of 10,362 images obtained. All of the aerial images were processed and, on average, 23.9 % of the area was classified as medusahead infested, with 76.1 % without infestation. Each image covered 215 ha (531 acres), with 60% overlap, at a cost of $ 0.54/km². Our study also employed mobile mapping technology to map medusahead on the ground by digitizing infestations using a laptop computer equipped with a GPS antenna and GIS software. Mobile mapping was also done from aircraft, but yielded coarser infestation maps, as the observation distance was greater. These maps covered the full study area. Aerial reconnaissance and mobile survey is cost effective, because thousands of digital images were collected, automatically positioned, and stored

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Design of a lightweight, modular robotic vehicle for the sustainable intensification of broadacre agriculture

This thesis presents the design process and the prototyping of a lightweight, modular robotic vehicle for the sustainable intensification of broadacre agriculture. Achieved by the joint operation of multiple autonomous vehicles to improve energy consumption, reduce labour, and increase efficiency in the application of inputs for the management of crops. The Small Robotic Farm Vehicle (SRFV) is a lightweight and energy efficient robotic vehicle with a configurable, modular design. It is capable of undertaking a range of agricultural tasks, including fertilising and weed management through mechanical intervention and precision spraying, whilst being more than an order of magnitude lower in weight than existing broadacre agricultural equipment