Vision systems for autonomous aircraft guidance

Moore, Richard J. D. (2012). Vision systems for autonomous aircraft guidance PhD Thesis, Queensland Brain Institute, The University of Queensland.

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Click to show the corresponding preview av01_collisionavoidance.mp4 av01_collisionavoidance.mp4 Click to show the corresponding preview/stream video/mp4 3.51MB 21
Click to show the corresponding preview av02_terrainfollowing.mp4 av02_terrainfollowing.mp4 Click to show the corresponding preview/stream video/mp4 16.93MB 6
Click to show the corresponding preview av03_terrainfollowing.mp4 av03_terrainfollowing.mp4 Click to show the corresponding preview/stream video/mp4 15.34MB 2
Click to show the corresponding preview av04_opticflowstereo.mp4 av04_opticflowstereo.mp4 Click to show the corresponding preview/stream video/mp4 23.15MB 3
Click to show the corresponding preview av05_horizonattitude.mp4 av05_horizonattitude.mp4 Click to show the corresponding preview/stream video/mp4 51.10MB 3
Click to show the corresponding preview av06_visualheading.mp4 av06_visualheading.mp4 Click to show the corresponding preview/stream video/mp4 42.98MB 4
Click to show the corresponding preview av07_visualodometry.mp4 av07_visualodometry.mp4 Click to show the corresponding preview/stream video/x-msvideo 40.61MB 7
Click to show the corresponding preview av08_visualwind.mp4 av08_visualwind.mp4 Click to show the corresponding preview/stream video/mp4 15.62MB 0
Click to show the corresponding preview av09_autolanding.mp4 av09_autolanding.mp4 Click to show the corresponding preview/stream video/mp4 13.54MB 3
Click to show the corresponding preview av10_immelmann.mp4 av10_immelmann.mp4 Click to show the corresponding preview/stream video/mp4 41.71MB 2
Click to show the corresponding preview av11_targettracking.mp4 av11_targettracking.mp4 Click to show the corresponding preview/stream video/mp4 37.79MB 6
Click to show the corresponding preview av12_cartracking.mp4 av12_cartracking.mp4 Click to show the corresponding preview/stream video/mp4 13.12MB 9
Click to show the corresponding preview av13_staticintercept.mp4 av13_staticintercept.mp4 Click to show the corresponding preview/stream video/mp4 3.80MB 3
Click to show the corresponding preview av14_movingintercept.mp4 av14_movingintercept.mp4 Click to show the corresponding preview/stream video/mp4 4.75MB 3
Click to show the corresponding preview av15_visualinterception.mp4 av15_visualinterception.mp4 Click to show the corresponding preview/stream video/mp4 43.10MB 4
Click to show the corresponding preview av16_accelintercept.mp4 av16_accelintercept.mp4 Click to show the corresponding preview/stream video/mp4 3.67MB 4
Click to show the corresponding preview av17_landingintercept.mp4 av17_landingintercept.mp4 Click to show the corresponding preview/stream video/mp4 9.91MB 4
s4161002_media.zip s4161002_media.zip Click to show the corresponding preview/stream application/zip 373.84MB 29
s4161002_phd_finalsubmission.pdf Thesis full text application/pdf 11.36MB 685
Author Moore, Richard J. D.
Thesis Title Vision systems for autonomous aircraft guidance
School, Centre or Institute Queensland Brain Institute
Institution The University of Queensland
Publication date 2012
Thesis type PhD Thesis
Open Access Status Other
Supervisor Mandyam V. Srinivasan
Gordon F. Wyeth
Total pages 245
Total colour pages 43
Total black and white pages 202
Language eng
Subjects 080101 Adaptive Agents and Intelligent Robotics
080104 Computer Vision
080106 Image Processing
Formatted abstract
Unmanned aerial vehicles (UAVs) are increasingly being used for a wide variety of civil and commercial applications such as infrastructure inspection and maintenance, search and rescue, mapping and cartography, as well as agricultural and environmental monitoring, to name just a few. Unmanned aircraft are suited to these roles because they can be smaller and lighter than manned aircraft and hence cheaper to operate, as well as being able to perform dull or repetitive tasks with greater precision than human operators, and dangerous tasks with greater safety. With the expanding set of roles for UAVs, there is an increasing need for them to be able to fly with a degree of low-level autonomy, thus freeing up their human controllers to concentrate on high-level decisions. 
  
     Modern UAVs control their position and orientation in space using technologies such as the Global Positioning System (GPS) and attitude and heading reference systems (AHRSs). They are unable to detect or avoid other objects or vehicles using these systems only, however, rendering them incapable of operating autonomously in near-Earth environments or around other moving vehicles. In these situations the aircraft must be able to monitor its surroundings continuously. Active proximity sensors, such as laser range-finders or radar, can be bulky, stealth-compromising, high-power, and low-bandwidth – limiting their utility for small-scale UAVs. There is considerable benefit to be gained, therefore, by designing guidance systems that use passive sensing, such as vision.

     This thesis builds on recent research into biological vision-based flight control strategies to demonstrate that such bioinspired methods can offer dramatically improved sensing and control efficiencies over more complex computer vision-based approaches. Furthermore, this thesis establishes that wide-angle vision systems enable a broad array of sensing and guidance strategies, which can be implemented in parallel, in turn enabling complex flight behaviours that would traditionally require sensing and processing architectures incompatible with small-scale UAVs.

     Two wide-angle vision systems are developed during the course of this research as well as a number of novel vision-based sensing and guidance algorithms, enabling detection of oncoming obstacles, estimation of attitude and altitude, long-term tracking of features, and interception of independently moving objects. Using these systems, complex capabilities such as terrain following at low altitude, aerobatics, landing in an uncontrolled environment, as well as tracking and interception of an independently moving object are all demonstrated for the first time using only computing resources available on board a small-scale UAV and using only vision for all sensing and guidance.   

     The findings of this thesis contribute towards a greater understanding of the minimum requirements – in terms of sensing and guidance architectures – for complex UAV behaviours; and the design methodologies proposed herein represent an important step towards full autonomy for small-scale airborne platforms, thereby contributing towards exploitation of UAVs for civil and commercial applications and bringing autonomous UAVs a step closer to the remarkable capabilities of their biological counterparts.
Keyword Visual guidance
Autonomous robotics
Unmanned aerial vehicle (UAV)
Bioinspired systems

Document type: Thesis
Collections: UQ Theses (RHD) - Official
UQ Theses (RHD) - Open Access
 
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Created: Thu, 15 Nov 2012, 02:25:40 EST by Richard Moore on behalf of University of Queensland Graduate School