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Sobre
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Sobre

Filipe Neves dos Santos nasceu em São Paio de Oleiros, em Portugal, em 1979. Doutorado em engenharia eletrotécnica e computadores (2014) pela Faculdade de Engenharia da Universidade do Porto (FEUP), Mestrado em engenharia eletrotécnica e computadores – automação e robótica (2007) pelo Instituto Superior Técnico (IST) da Universidade Técnica de Lisboa, licenciado em engenharia eletrotécnica e computadores (2003) pelo Instituto Superior de Engenharia do Porto (ISEP). Profissionalmente é apaixonado pela investigação e desenvolvimento de soluções robóticas e automatização que permitam resolver problemas reais, desejos e necessidades da nossa sociedade e contribuir para a autossustentabilidade e justiça da economia global. Neste momento a sua principal linha de investigação centra-se no desenvolvimento de soluções robotizadas para o setor agrícola e florestal, onde é necessária uma maior eficiência para a nossa autossustentabilidade mundial. Em 2013, considerando a realidade de Portugal e os principais roteiros de inovação, estruturou um roteiro de investigação centrado no desenvolvimento de robótica e sistemas inteligentes para o contexto agrícola e florestal. Nomeadamente, em contextos de declive acentuado e sem acesso a GPS/GNSS, onde são requeridas a execução de tarefas tais como: monitorização (por terra), pulverização de precisão, logística, poda e colheita seletiva. A execução eficiente destas tarefas depende em grande parte da robustez dos sistemas robóticos específicos, tais como:  Perceção visual;- Navegação (localização, mapeamento e planeamento de caminhos seguros); e  Manipulação e ferramentas especificas. A sua formação em engenharia MSc (fusão sensorial e GPS/GNSS), PhD (mapeamento e localização semântica), experiência de 4 anos como empreendedor (startup tecnológica), participação e coordenação de projetos de investigação na área da robótica durante mais de 12 anos, 5 anos de experiência em tarefas de contabilidade e gestão (empresa familiar), e 6 anos como técnico de eletrónica fornecerão o saber saber e saber fazer para que possa contribuir para o sucesso do futuro da robótica agrícola.

Tópicos
de interesse
Detalhes

Detalhes

023
Publicações

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Autores
Aguiar, AS; dos Santos, FN; Sobreira, H; Cunha, JB; Sousa, AJ;

Publicação
Robotics and Autonomous Systems

Abstract

2021

Potential Non-Invasive Technique for Accessing Plant Water Contents Using a Radar System

Autores
Santos, LC; dos Santos, FN; Morais, R; Duarte, C;

Publicação
Agronomy

Abstract
Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution—for the automotive industry—to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.

2021

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

Autores
Aguiar, AS; Monteiro, NN; dos Santos, FN; Pires, EJS; Silva, D; Sousa, AJ; Boaventura Cunha, J;

Publicação
Agriculture

Abstract
The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

2021

Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Autores
da Silva, DQ; Aguiar, AS; dos Santos, FN; Sousa, AJ; Rabino, D; Biddoccu, M; Bagagiolo, G; Delmastro, M;

Publicação
Agriculture

Abstract
Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

2021

A Camera to LiDAR calibration approach through the Optimization of Atomic Transformations

Autores
Pinto de Aguiar, AS; Riem de Oliveira, MA; Pedrosa, EF; Neves dos Santos, FB;

Publicação
Expert Systems with Applications

Abstract

Teses
supervisionadas

2020

Advanced 2.5D Path Planning for agricultural robots

Autor
Luís Carlos Feliz Santos

Instituição
UTAD

2020

Grasping and manipulation with active perception for open-field agricultural robotics

Autor
Sandro Augusto Costa Magalhães

Instituição
UP-FEUP

2020

Localization and Mapping based on Semantic and Multi-Layer Maps Concepts

Autor
André Silva Pinto de Aguiar

Instituição
UP-FEUP

2018

Design and construction of cost effective VTOL drone for agricultural and forestry application

Autor
Ahmad Safaee

Instituição
UP-FEUP

2018

Odometria visual em robôs para a agricultura com câmara(s) com lentes olho de peixe

Autor
Sérgio Miguel Vieira Pinto

Instituição
UP-FEUP