Douthwaite, J.A., De Freitas, A. and Mihaylova, L.S. (2017) An Interval Approach to Multiple Unmanned Aerial Vehicle Collision Avoidance. In: Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2017. 11th Symposium Sensor Data Fusion: Trends, Solutions, and Applications, 10-12 Oct 2017, Bonn, Germany. IEEE
Abstract
Small/micro Unmanned Aerial Systems (UAVs) require the ability to operate with constraints of a diverse, automated airspace where obstacle telemetry is denied. This paper proposes a novel Sense, Detect and Avoid (SDA) algorithm with inherit resilience to sensor uncertainty. This is achieved through the interval geometric formulation of the avoidance problem, which by the use of interval analysis, can be extended to consider multiple obstacles. The approach is shown to demonstrate the ability to both tolerate sensor uncertainty and enact generated 3D avoidance trajectories. Monte-Carlo simulations demonstrate successful avoidance rates of 88%, 96% and 91% in two example collision scenarios and one multi-agent conflict scenario respectively.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Collision avoidance; unmanned aerial systems; interval analysis; sensor uncertainty; interval geometry |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Sep 2017 09:54 |
Last Modified: | 27 Jul 2020 08:27 |
Published Version: | https://doi.org/10.1109/SDF.2017.8126384 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/SDF.2017.8126384 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121512 |