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Projeto de investigação
Aggregate Farming in the Cloud
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Autores
Publicações
Precision landing for low-maintenance remote operations with UAVs
Publication . Moreira, Miguel; Azevedo, Fábio; Ferreira, André; Pedro, Dário; Matos-Carvalho, João; Ramos, Álvaro; Loureiro, Rui; Campos, Luís; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas; DEE - Departamento de Engenharia Electrotécnica e de Computadores; MDPI - Multidisciplinary Digital Publishing Institute
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.
Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques
Publication . Pedro, Dário; Matos-Carvalho, João P.; Fonseca, José M.; Mora, André; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DEE - Departamento de Engenharia Electrotécnica e de Computadores; DEE2010-C1 Sistemas Digitais e Percepcionais; Molecular Diversity Preservation International (MDPI)
Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
783221