A computationally intellgent framework for UAV forced landings
Fitzgerald, Daniel L., Walker, Rodney A., & Campbell, Duncan A. (2005) A computationally intellgent framework for UAV forced landings. In IASTED Computational Intelligence Conference, July 2005, Calgary, Canada.
A computationally intelligent framework has been developed for the forced landing problem for uninhabited airborne vehicles (UAVs). This framework locates landing areas within an image that are of the appropriate geometrical size and free of obstacles. The surface-type of the candidate landing areas are classified into categories such as grass, trees and water. The classification results are combined with other information such as, the spatial relationships between candidate areas, the presence of moving objects (for example cars and people) and the objects in surrounding the areas, to nominate candidate UAV forced landing sites.
A discussion is presented that shows that a type-2 fuzzy-based approach is expected to be useful in resolving data-set uncertainties allowing a reliable UAV forced landing site recommendation to be made. Examples of data-set uncertainties include the surface type classification and the models of motion of various objects.
Results are presented showing the successful location of appropriate candidate UAV landing sites. A success rate of 90% has been achieved using a neural network classification approach and based on the testing of 500 images. These results are based on actual flight imagery collected from a Cessna 172 flight over Brisbane, Australia.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Uninhabited airborne vehicles (UAV), UAV forced landing, UAV safety, computationally intelligent framework, machine vision, fuzzy systems, radial basis probabilistic neural networks, classification|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Aerospace Engineering not elsewhere classified (090199)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2005 ACTA Press|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||08 Nov 2006|
|Last Modified:||29 Feb 2012 23:16|
Repository Staff Only: item control page