Sabo, C. and Simpson, A. (2016) Quadcopter Obstacle Avoidance using Biomimetic Algorithms. In: AIAA Infotech @ Aerospace, AIAA SciTec. AIAA Infotech@Aerospace, 04-08 Jan 2016, San Diego, CA. American Institute of Aeronautics and Astronautics ISBN 978-1-62410-388-9
Abstract
Unmanned Micro Aerial Vehicles (MAVs) have the potential to operate in diverse environments but are limited by the lack of robust algorithms for autonomous flight. This is largely due to the sensing and processing requirements that exceed the weight and power limitations of this hardware. Recent research has highlighted the potential to overcome these constraints by looking to the natural world, in particular to the possibilities of using optical flow. This work presents a novel biomimetic algorithm that uses optical flow data generated from the on-board camera of a quadcopter MAV to avoid obstacles in flight. Simulation results are presented showing the algorithm performance in a range of flying scenarios. This work also highlights the huge potential of using low resolution sensors and lightweight algorithms in the field of autonomous vehicle control.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 AIAA. This is an author produced version of a paper subsequently published in 'AIAA Infotech @ Aerospace' available at http://dx.doi.org/10.2514/6.2016-0403. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Apr 2016 11:50 |
Last Modified: | 19 Dec 2022 13:33 |
Published Version: | http://arc.aiaa.org/doi/abs/10.2514/6.2016-0403 |
Status: | Published |
Publisher: | American Institute of Aeronautics and Astronautics |
Identification Number: | 10.2514/6.2016-0403 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:96477 |