JOURNAL ARTICLE
Autonomous Drone Landing: Marked Landing Pads and Solidified Lava Flows.
Published In: International Journal of Semantic Computing, 2024, v. 18, n. 2. P. 283 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Springer, Joshua; Kyas, Marcel 3 of 3
Abstract
Landing is the most challenging and risky aspect of basic multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than global positioning system (GPS) can provide (e.g. package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the Rover-Aerial Vehicle Exploration Network (RAVEN) project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone's behavior, mission structure, or operational environments. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Semantic Computing. 2024/06, Vol. 18, Issue 2, p283
- Document Type:Article
- Subject Area:Geology
- Publication Date:2024
- ISSN:1793351X
- DOI:10.1142/S1793351X24300061
- Accession Number:178334265
- Copyright Statement:Copyright of International Journal of Semantic Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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