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Sobre

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

  • Nome

    Filipe Neves Santos
  • Cargo

    Coordenador de TEC4
  • Desde

    20 setembro 2011
046
Publicações

2024

Robotic Arm Development for a Quadruped Robot

Autores
Lopes, S; Moreira, AP; Silva, F; Santos, F;

Publicação
Lecture Notes in Networks and Systems

Abstract
Quadruped robots have gained significant attention in the robotics world due to their capability to traverse unstructured terrains, making them advantageous in search and rescue and surveillance operations. However, their utility is substantially restricted in situations where object manipulation is necessary. A potential solution is to integrate a robotic arm, although this can be challenging since the arm’s addition may unbalance the whole system, affecting the quadruped locomotion. To address this issue, the robotic arm must be adapted to the quadruped robot, which is not viable with commercially available products. This paper details the design and development of a robotic arm that has been specifically built to integrate with a quadruped robot to use in a variety of agricultural and industrial applications. The design of the arm, including its physical model and kinematic configuration, is presented. To assess the effectiveness of the prototype, a simulation was conducted with a motion-planning algorithm based on the arm’s inverse kinematics. The simulation results confirm the system’s stability and the functionality of the robotic arm’s movement. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

Autores
Sarmento, J; dos Santos, FN; Aguiar, AS; Filipe, V; Valente, A;

Publicação
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS

Abstract
Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

2024

Reagentless Vis-NIR Spectroscopy Point-of-Care for Feline Total White Blood Cell Counts

Autores
Barroso, TG; Queirós, C; Monteiro-Silva, F; Santos, F; Gregório, AH; Martins, RC;

Publicação
BIOSENSORS-BASEL

Abstract
Spectral point-of-care technology is reagentless with minimal sampling (<10 mu L) and can be performed in real-time. White blood cells are non-dominant in blood and in spectral information, suffering significant interferences from dominant constituents such as red blood cells, hemoglobin and billirubin. White blood cells of a bigger size can account for 0.5% to 22.5% of blood spectra information. Knowledge expansion was performed using data augmentation through the hybridization of 94 real-world blood samples into 300 synthetic data samples. Synthetic data samples are representative of real-world data, expanding the detailed spectral information through sample hybridization, allowing us to unscramble the spectral white blood cell information from spectra, with correlations of 0.7975 to 0.8397 and a mean absolute error of 32.25% to 34.13%; furthermore, we achieved a diagnostic efficiency between 83% and 100% inside the reference interval (5.5 to 19.5 x 10(9) cell/L), and 85.11% for cases with extreme high white blood cell counts. At the covariance mode level, white blood cells are quantified using orthogonal information on red blood cells, maximizing sensitivity and specificity towards white blood cells, and avoiding the use of non-specific natural correlations present in the dataset; thus, the specifity of white blood cells spectral information is increased. The presented research is a step towards high-specificity, reagentless, miniaturized spectral point-of-care hematology technology for Veterinary Medicine.

2023

Nano Aerial Vehicles for Tree Pollination

Autores
Pinheiro, I; Aguiar, A; Figueiredo, A; Pinho, T; Valente, A; Santos, F;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command.

2023

Safety Standards for Collision Avoidance Systems in Agricultural Robots - A Review

Autores
Martins, JJ; Silva, M; Santos, F;

Publicação
ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Abstract
To produce more food and tackle the labor scarcity, agriculture needs safer robots for repetitive and unsafe tasks (such as spraying). The interaction between humans and robots presents some challenges to ensure a certifiable safe collaboration between human-robot, a reliable system that does not damage goods and plants, in a context where the environment is mostly dynamic, due to the constant environment changes. A well-known solution to this problem is the implementation of real-time collision avoidance systems. This paper presents a global overview about state of the art methods implemented in the agricultural environment that ensure human-robot collaboration according to recognised industry standards. To complement are addressed the gaps and possible specifications that need to be clarified in future standards, taking into consideration the human-machine safety requirements for agricultural autonomous mobile robots.

Teses
supervisionadas

2022

Localization and Mapping Based on Semantic and Multi-layer Maps Concepts

Autor
André Silva Pinto de Aguiar

Instituição
UTAD

2020

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

Autor
Sandro Augusto Costa Magalhães

Instituição
UP-FEUP

2020

Advanced 2.5D Path Planning for agricultural robots

Autor
Luís Carlos Feliz Santos

Instituição
UTAD

2020

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

Autor
André Silva Pinto de Aguiar

Instituição
UTAD

2018

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

Autor
Sérgio Miguel Vieira Pinto

Instituição
UP-FEUP