A laser obstacle warning and avoidance system for unmanned aircraft sense-and-avoid

Sabatini, R, Gardi, A and Ramasamy, S 2014, 'A laser obstacle warning and avoidance system for unmanned aircraft sense-and-avoid', Applied Mechanics and Materials, vol. 629, pp. 355-360.

Document type: Journal Article
Collection: Journal Articles

Attached Files
Name Description MIMEType Size
n2006049366.pdf Accepted Manuscript application/pdf 487.41KB
Title A laser obstacle warning and avoidance system for unmanned aircraft sense-and-avoid
Author(s) Sabatini, R
Gardi, A
Ramasamy, S
Year 2014
Journal name Applied Mechanics and Materials
Volume number 629
Start page 355
End page 360
Total pages 6
Publisher Scientific.Net
Abstract This paper presents an overview of the research activities performed to develop a new scaled variant of the Laser Obstacle Avoidance and Monitoring (LOAM) system for small-to-medium size Unmanned Aircraft (UA) platforms. This LOAM variant (LOAM+) is proposed as one of the non-cooperative sensors employed in the UA Sense-and-Avoid (SAA) system. After a brief description of the LOAM system architecture, the mathematical models developed for obstacle avoidance and calculation of alternative flight path are presented. Additionally, a new formulation is adopted for defining the uncertainty volumes associated with the detected obstacles. Simulation case studies are carried out to evaluate the performances of the avoidance trajectory generation and optimisation algorithms, which demonstrate the ability of LOAM+ to effectively detect and avoid fixed low-level obstacles in the intended path.
Subject Avionics
Aerospace Engineering not elsewhere classified
Keyword(s) LIDAR
Low-level Flight
Obstacle Detection
Obstacle Warning
Obstacle Avoidance
Uncertainty Volume
Unmanned Aircraft
DOI - identifier 10.4028/www.scientific.net/AMM.629.355
Copyright notice © 2014 Trans Tech Publications
ISSN 1662-7482
Version Filter Type
Citation counts: Scopus Citation Count Cited 15 times in Scopus Article | Citations
Altmetric details:
Access Statistics: 424 Abstract Views, 239 File Downloads  -  Detailed Statistics
Created: Wed, 14 Jan 2015, 09:02:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us