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About

Filipe Neves dos Santos was born in São Paio de Oleiros, Portugal, in 1979. He olds a Licenciatura (5-year degree) in Electrical and Computer Engineering in 2003 from Instituto Superior de Engenharia do Porto (ISEP), a M.Sc. in Electrical and Computer Engineering from the Instituto Superior Técnico (IST) da Universidade Técnica de Lisboa, in 2007, and received the PhD degree in Electrical and Computer Engineering at the Faculdade de Engenharia (FEUP), Universidade do Porto, Portugal, in 2014. His professional passion is to develop autonomous robots and machinery to solve real problems, desires and needs of our society and to contribute for self-sustainability and fairness of the global economy. Actually, He is focused in developing and researching robotic solutions for agriculture and forestry sector, where is required a higher efficiency for our world self-sustainability. Considering his closer regional reality, he have setup the goal to promote agricultural robotic based projects and develop robots that can operate fully autonomously and safely in steep-slope scenarios, which is a common reality of North of Portugal and in other large number of world regions. As so, he is interested in explore and develop robots for specific agricultural and forestall tasks such as: monitoring (by ground), spraying, logistics, pruning, and selective harvesting. The successfully execution of these task is largely dependent on the robustness of specific robotic systems, such as: - Visual Perception; - Navigation (localization, mapping and path planning); and - Manipulation and end tools. For that reason Visual Perception and Navigation are his main research fields inside of robotics research. His formation in Electronics and Computer Engineer (Bachelor (old-one of 5 years) MSc (sensor fusion), PhD (semantic mapping) ), experience of 4 years as entrepreneur (technological startup), 8 year as robotics researcher, 5 years as manager (in supporting tasks in a family enterprise), and 6 year as electronics technician will help him to successfully contribute for the agricultural and forestall robotics future.

Interest
Topics
Details

Details

018
Publications

2020

Deep Learning Applications in Agriculture: A Short Review

Authors
Santos, L; Santos, FN; Oliveira, PM; Shinde, P;

Publication
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract

2020

Forest Robot and Datasets for Biomass Collection

Authors
Reis, R; dos Santos, FN; Santos, L;

Publication
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract

2020

Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

Authors
Santos, L; Santos, F; Mendes, J; Costa, P; Lima, J; Reis, R; Shinde, P;

Publication
Robotica

Abstract
SummarySteep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

2020

Visual Trunk Detection Using Transfer Learning and a Deep Learning-based Coprocessor

Authors
Aguiar, AS; Dos Santos, FN; Miranda De Sousa, AJM; Oliveira, PM; Santos, LC;

Publication
IEEE Access

Abstract

2019

Online object trajectory classification using FPGA-SoC devices

Authors
Shinde, P; Machado, P; Santos, FN; McGinnity, TM;

Publication
Advances in Intelligent Systems and Computing

Abstract
Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work. © Springer Nature Switzerland AG 2019.

Supervised
thesis

2018

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

Author
Ahmad Safaee

Institution
UP-FEUP

2018

Técnicas avançadas de monitorização em aplicações de agricultura de precisão

Author
Jorge Miguel Ferreira da Silva Mendes

Institution
UTAD

2018

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

Author
Sérgio Miguel Vieira Pinto

Institution
UP-FEUP

2017

Manipulador robótico para poda automática (Projecto ROMOVI)

Author
José Nuno Gomes de Brito

Institution
UP-FEUP

2017

Paralelização de Algoritmos de Processamento de Imagem em Contexto de Vinha

Author
Filipe Alberto Sampaio Azevedo

Institution
UP-FEUP