A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems
This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/23/9230 |
| _version_ | 1797462134312927232 |
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| author | Hae-In Lee Hyo-Sang Shin Antonios Tsourdos |
| author_facet | Hae-In Lee Hyo-Sang Shin Antonios Tsourdos |
| author_sort | Hae-In Lee |
| collection | DOAJ |
| description | This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms. |
| first_indexed | 2024-03-09T17:33:08Z |
| format | Article |
| id | doaj.art-b6a25a57b1d44a9d86fe05126f7c6deb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:33:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b6a25a57b1d44a9d86fe05126f7c6deb2023-11-24T12:10:43ZengMDPI AGSensors1424-82202022-11-012223923010.3390/s22239230A Probabilistic–Geometric Approach for UAV Detection and Avoidance SystemsHae-In Lee0Hyo-Sang Shin1Antonios Tsourdos2School of Aerospace, Transport and Manufacturing, Cranfield University, College Road, Cranfield MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing, Cranfield University, College Road, Cranfield MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing, Cranfield University, College Road, Cranfield MK43 0AL, UKThis paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms.https://www.mdpi.com/1424-8220/22/23/9230detection and avoidanceUnmanned Aerial Vehicle (UAV)collision probabilitydifferential geometry |
| spellingShingle | Hae-In Lee Hyo-Sang Shin Antonios Tsourdos A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems Sensors detection and avoidance Unmanned Aerial Vehicle (UAV) collision probability differential geometry |
| title | A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems |
| title_full | A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems |
| title_fullStr | A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems |
| title_full_unstemmed | A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems |
| title_short | A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems |
| title_sort | probabilistic geometric approach for uav detection and avoidance systems |
| topic | detection and avoidance Unmanned Aerial Vehicle (UAV) collision probability differential geometry |
| url | https://www.mdpi.com/1424-8220/22/23/9230 |
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