FFAU—Framework for Fully Autonomous UAVs
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. Howe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3533 |
| _version_ | 1797549538101166080 |
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| author | Dário Pedro João P. Matos-Carvalho Fábio Azevedo Ricardo Sacoto-Martins Luís Bernardo Luís Campos José M. Fonseca André Mora |
| author_facet | Dário Pedro João P. Matos-Carvalho Fábio Azevedo Ricardo Sacoto-Martins Luís Bernardo Luís Campos José M. Fonseca André Mora |
| author_sort | Dário Pedro |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world. |
| first_indexed | 2024-03-10T15:16:01Z |
| format | Article |
| id | doaj.art-9fe767b5309a49249e5b07f97d191ea2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:16:01Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9fe767b5309a49249e5b07f97d191ea22023-11-20T18:54:08ZengMDPI AGRemote Sensing2072-42922020-10-011221353310.3390/rs12213533FFAU—Framework for Fully Autonomous UAVsDário Pedro0João P. Matos-Carvalho1Fábio Azevedo2Ricardo Sacoto-Martins3Luís Bernardo4Luís Campos5José M. Fonseca6André Mora7PDMFC, 1300-609 Lisbon, PortugalCOPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, PortugalBeyond Vision, 2610-161 Ílhavo, PortugalCentre of Technology and Systems, UNINOVA, 2829-516 Caparica, PortugalElectrical Engineering Department, FCT, NOVA University of Lisbon, 2829-516 Caparica, PortugalPDMFC, 1300-609 Lisbon, PortugalCentre of Technology and Systems, UNINOVA, 2829-516 Caparica, PortugalCentre of Technology and Systems, UNINOVA, 2829-516 Caparica, PortugalUnmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.https://www.mdpi.com/2072-4292/12/21/3533UAVframeworkdronescollision avoidanceresilienceartificial intelligence |
| spellingShingle | Dário Pedro João P. Matos-Carvalho Fábio Azevedo Ricardo Sacoto-Martins Luís Bernardo Luís Campos José M. Fonseca André Mora FFAU—Framework for Fully Autonomous UAVs Remote Sensing UAV framework drones collision avoidance resilience artificial intelligence |
| title | FFAU—Framework for Fully Autonomous UAVs |
| title_full | FFAU—Framework for Fully Autonomous UAVs |
| title_fullStr | FFAU—Framework for Fully Autonomous UAVs |
| title_full_unstemmed | FFAU—Framework for Fully Autonomous UAVs |
| title_short | FFAU—Framework for Fully Autonomous UAVs |
| title_sort | ffau framework for fully autonomous uavs |
| topic | UAV framework drones collision avoidance resilience artificial intelligence |
| url | https://www.mdpi.com/2072-4292/12/21/3533 |
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