Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios
The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/13 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588636239953920 |
|---|---|
| author | Paula Seoane Enrique Aldao Fernando Veiga-López Higinio González-Jorge |
| author_facet | Paula Seoane Enrique Aldao Fernando Veiga-López Higinio González-Jorge |
| author_sort | Paula Seoane |
| collection | DOAJ |
| description | The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed. |
| format | Article |
| id | doaj-art-8a9e017b76fc4a43bb1a8915defecdc2 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-8a9e017b76fc4a43bb1a8915defecdc22025-01-24T13:29:39ZengMDPI AGDrones2504-446X2024-12-01911310.3390/drones9010013Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision ScenariosPaula Seoane0Enrique Aldao1Fernando Veiga-López2Higinio González-Jorge3Aerolab, Insituto de Física e Ciencias Aeroespaciais (IFCAE), Universidade de Vigo, Campus de As Lagoas, E-32004 Ourense, SpainAerolab, Insituto de Física e Ciencias Aeroespaciais (IFCAE), Universidade de Vigo, Campus de As Lagoas, E-32004 Ourense, SpainAerolab, Insituto de Física e Ciencias Aeroespaciais (IFCAE), Universidade de Vigo, Campus de As Lagoas, E-32004 Ourense, SpainAerolab, Insituto de Física e Ciencias Aeroespaciais (IFCAE), Universidade de Vigo, Campus de As Lagoas, E-32004 Ourense, SpainThe deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed.https://www.mdpi.com/2504-446X/9/1/13unmanned aerial vehicleadvanced air mobilitytactical deconflictionLiDARdetect and avoid |
| spellingShingle | Paula Seoane Enrique Aldao Fernando Veiga-López Higinio González-Jorge Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios Drones unmanned aerial vehicle advanced air mobility tactical deconfliction LiDAR detect and avoid |
| title | Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios |
| title_full | Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios |
| title_fullStr | Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios |
| title_full_unstemmed | Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios |
| title_short | Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios |
| title_sort | assessment of lidar based sensing technologies in bird drone collision scenarios |
| topic | unmanned aerial vehicle advanced air mobility tactical deconfliction LiDAR detect and avoid |
| url | https://www.mdpi.com/2504-446X/9/1/13 |
| work_keys_str_mv | AT paulaseoane assessmentoflidarbasedsensingtechnologiesinbirddronecollisionscenarios AT enriquealdao assessmentoflidarbasedsensingtechnologiesinbirddronecollisionscenarios AT fernandoveigalopez assessmentoflidarbasedsensingtechnologiesinbirddronecollisionscenarios AT higiniogonzalezjorge assessmentoflidarbasedsensingtechnologiesinbirddronecollisionscenarios |