Dr RASA REMENYTE-PRESCOTT R.REMENYTE-PRESCOTT@NOTTINGHAM.AC.UK
ASSOCIATE PROFESSOR
An efficient phased mission reliability analysis for autonomous vehicles
Remenyte-Prescott, Rasa; Andrews, John; Chung, Paul
Authors
Professor JOHN ANDREWS john.andrews@nottingham.ac.uk
PROFESSOR OF INFRASTRUCTURE ASSET MANAGEMENT
Paul Chung
Abstract
Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees.
Unmanned Autonomous Vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or sub-systems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real-time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results.
Citation
Remenyte-Prescott, R., Andrews, J., & Chung, P. (2010). An efficient phased mission reliability analysis for autonomous vehicles. Reliability Engineering and System Safety, 95(3), https://doi.org/10.1016/j.ress.2009.10.002
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2010 |
Deposit Date | Jul 31, 2014 |
Publicly Available Date | Jul 31, 2014 |
Journal | Reliability Engineering and System Safety |
Electronic ISSN | 0951-8320 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 95 |
Issue | 3 |
DOI | https://doi.org/10.1016/j.ress.2009.10.002 |
Public URL | https://nottingham-repository.worktribe.com/output/1013077 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S0951832009002397 |
Additional Information | NOTICE: this is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering & System Safety, 95(3), (2010), doi: 10.1016/j.ress.2009.10.002 |
Files
RESS_2010_UAVs.pdf
(266 Kb)
PDF
You might also like
An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
(2024)
Journal Article
A novel fault detection and diagnostic Petri net methodology for dynamic systems
(2023)
Journal Article
Intelligent and adaptive asset management model for railway sections using the iPN method
(2023)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search