Collision avoidance for safe structure inspection with multirotor UAV

Azevedo, Fábio Mícolis de; Oliveira, A.; Dias, André; Almeida, J.; Moreira, M.; Santos, Tiago C. dos; Ferreira, André; Martins, Alfredo; Silva, Eduardo

doi:10.1109/ecmr.2017.8098719

Cited by 17 publications

(13 citation statements)

References 15 publications

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“…Multirotor UAVs are extremely flexible and are used in a variety of operations such as agriculture [32], search and rescue, or inspection [33,34]. However, this flexibility usually comes with a significant limitation: limited range and flight time due to the low battery storage capacity.…”

Section: Algorithms For Uav Precision Landingmentioning

confidence: 99%

Precision Landing for Low-Maintenance Remote Operations with UAVs

Moreira¹,

Azevedo

Ferreira³

et al. 2021

Drones

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This work proposes a fully integrated ecosystem composed of three main components with a complex goal: to implement an autonomous system with a UAV requiring little to no maintenance and capable of flying autonomously. For this goal, was developed an autonomous UAV, an online platform capable of its management and a landing platform to enclose and charge the UAV after flights. Furthermore, a precision landing algorithm ensures no need for human intervention for long-term operations.

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Section: Algorithms For Uav Precision Landingmentioning

confidence: 99%

Precision Landing for Low-Maintenance Remote Operations with UAVs

Moreira¹,

Azevedo

Ferreira³

et al. 2021

Drones

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“…Hert et al [39] applied 2D knowledge to the 3D environment and some other methods to approach navigating with obstacle avoidance. Another method consists on interpolating the goal points given by the user and setting escape callbacks that enter in play whenever an obstacle is detected [40]. Section 3 will add additional detail regarding this topic.…”

Section: Collision Aware Plannermentioning

confidence: 99%

FFAU—Framework for Fully Autonomous UAVs

et al. 2020

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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.

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“…Regarding the specific application of power line elements inspection in the Drone Project, it was already developed and applied an obstacle detection module that is a variant of the work presented in [40]. Due to the low timestamp resolution of the point cloud points (a single timestamp for a complete scan of the LiDAR), some measured points can be transformed from the sensor to the world frame with a considerable error, generating an obstacle with low accuracy in position.…”

Section: Background and Motivationmentioning

confidence: 99%

“…A noisy background (e.g., with vegetation) can make the conductors visually undetectable by the pilot [22]. This arises the need for an autonomous module of obstacle detection and collision avoidance that is capable of actuating in both autonomous and assisted modes [40]. The detection of conductors can also be used for improving the navigation filter and line following maneuvers [22].…”

Section: Introductionmentioning

confidence: 99%

Real- Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles

Azevedo

Dias

Almeida

et al. 2019

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Esta dissertação foi resultado do contributo, direto ou indireto, de diversas pessoas, as quais deixo, desde já, uma palavra de sincera gratidão. Gostaria de começar os meus agradecimentos pelos Engenheiros José Almeida e André Dias, tanto pelo desafio apresentado, ao me atribuirem um tema ainda pouco explorado, como pelo auxílio prestado durante o desenvolvimento deste projeto. A todos os que fazem ou já fizeram parte do LSA, estou grato por tudo o que me ensinaram, pela ajuda que me forneceram e pelos momentos de convívio. Agradeço também aos meus colegas de curso pela caminhada conjunta, em que todos trocámos impressões e conhecimentos benéficos. Dirijo ainda um grande agradecimento aos meus colegas de grupo de trabalho, Alexandre Oliveira, André Ferreira, Miguel Moreira e Tiago Santos, tanto pelas conquistas obtidas, como pelo apoio constante que me deram, não esquecendo os momentos divertidos! Ao João Pedro Ribeiro fico grato pelas dicas, opiniões e ajuda na resolução de diversos problemas. Ao Denys Sytnyk, por ser o Dinis... Para com os meus pais, Avelino Azevedo e Eugénia Azevedo, estarei eternamente grato pelos esforços que fizeram, pela educação que me proporcionaram e pelas condições que criaram para que pudesse chegar até aqui. O meu reconhecimento finalé para alguém muito especial na minha vida: a minha namorada e companheira. Obrigado Carla Gomes! Pelo teu carinho, compreensão, apoio e motivação, nunca esquecendo das coisas de que abdicaste em prol do meu trabalho. Obrigado.

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Collision avoidance for safe structure inspection with multirotor UAV

Cited by 17 publications

References 15 publications

Precision Landing for Low-Maintenance Remote Operations with UAVs

Precision Landing for Low-Maintenance Remote Operations with UAVs

FFAU—Framework for Fully Autonomous UAVs

Real- Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles

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