| DOI | Resolve DOI: https://doi.org/10.1109/ICUAS51884.2021.9476755 |
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| Author | Search for: Maalouly, Anthony; Search for: Sharf, Inna; Search for: Mantegh, Iraj1 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Funder | Search for: Natural Sciences and Engineering Research Council of Canada; Search for: National Research Council of Canada |
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| Format | Text, Article |
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| Conference | 2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece |
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| Subject | heuristic algorithms; simulation; reinforcement learning; hardware; sensors; collision avoidance; vehicle dynamics |
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| Abstract | Quadrotors have seen a surge in popularity in many applications such as medical deliveries, surveying tasks, mapping tasks and much more. Urban operations, such as deliveries of parcels, require the quadrotor, referred to as the ownship in this paper, to be capable of autonomous navigation and collision avoidance, relying only on its onboard sensors. The focus of this paper is on the maneuvering of the ownship to avoid a potential collision with another neutral vehicle pursuing its own mission. We present a geometrical collision avoidance scheme based on the original 3DVO algorithm. The novelty of the improved 3DVO (I-3DVO) lies in its abilities to select an optimal collision avoidance plane and to vary the speed of the ownship to achieve collision avoidance under the short sensing distance conditions. We validate our proposed algorithm via a Matlab simulation accounting for the often neglected dynamics of the quadrotor ownship. |
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| Publication date | 2021-06-15 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | b6eab818-ec52-4689-be0d-0991a3761867 |
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| Record created | 2023-01-18 |
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| Record modified | 2023-03-16 |
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