Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Sobre
Download foto HD

Sobre

Nascido na cidade do Porto a 7 de Novembro de 1962, licenciou-se em 1986 em Engenharia Electrotécnica na Faculdade de Engenharia da Universidade do Porto (FEUP). Completou o seu mestrado em Engenharia Electrotécnica na especialidade de Sistemas em 1991 e o seu doutoramento na mesma área em 1998. Entre 1986 e 1998 foi contratado como Assistente no Departamento de Engenharia Electrotécnica e de Computadores da FEUP. Atualmente é Professor Associado com Agregação do referido Departamento, desenvolvendo a sua atividade de investigação no INESC TEC onde é coordenador do Centro de Robótica Industrial e Sistemas Inteligentes. As sua principais áreas de investigação são a Robótica e o Controlo de Processos.

Tópicos
de interesse
Detalhes

Detalhes

046
Publicações

2021

Robust human position estimation in cooperative robotic cells

Autores
Amorim, A; Guimares, D; Mendona, T; Neto, P; Costa, P; Moreira, AP;

Publicação
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Abstract
Robots are increasingly present in our lives, sharing the workspace and tasks with human co-workers. However, existing interfaces for human-robot interaction / cooperation (HRI/C) have limited levels of intuitiveness to use and safety is a major concern when humans and robots share the same workspace. Many times, this is due to the lack of a reliable estimation of the human pose in space which is the primary input to calculate the human-robot minimum distance (required for safety and collision avoidance) and HRI/C featuring machine learning algorithms classifying human behaviours / gestures. Each sensor type has its own characteristics resulting in problems such as occlusions (vision) and drift (inertial) when used in an isolated fashion. In this paper, it is proposed a combined system that merges the human tracking provided by a 3D vision sensor with the pose estimation provided by a set of inertial measurement units (IMUs) placed in human body limbs. The IMUs compensate the gaps in occluded areas to have tracking continuity. To mitigate the lingering effects of the IMU offset we propose a continuous online calculation of the offset value. Experimental tests were designed to simulate human motion in a human-robot collaborative environment where the robot moves away to avoid unexpected collisions with de human. Results indicate that our approach is able to capture the human's position, for example the forearm, with a precision in the millimetre range and robustness to occlusions.

2021

Reconfigurable Grasp Planning Pipeline with Grasp Synthesis and Selection Applied to Picking Operations in Aerospace Factories

Autores
Carvalho de Souza, JPC; Costa, CM; Rocha, LF; Arrais, R; Paulo Moreira, AP; Solteiro Pires, EJS; Boaventura Cunha, J;

Publicação
Robotics and Computer-Integrated Manufacturing

Abstract

2021

Autonomous wheelchair for patient’s transportation on healthcare institutions

Autores
Baltazar, AR; Petry, MR; Silva, MF; Moreira, AP;

Publicação
SN Applied Sciences

Abstract
AbstractThe transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

2021

Extrinsic sensor calibration methods for mobile robots: A short review

Autores
Sousa, RB; Petry, MR; Moreira, AP;

Publicação
Lecture Notes in Electrical Engineering

Abstract
Data acquisition is a critical task for localisation and perception of mobile robots. It is necessary to compute the relative pose between onboard sensors to process the data in a common frame. Thus, extrinsic calibration computes the sensor’s relative pose improving data consistency between them. This paper performs a literature review on extrinsic sensor calibration methods prioritising the most recent ones. The sensors types considered were laser scanners, cameras and IMUs. It was found methods for robot–laser, laser–laser, laser–camera, robot–camera, camera–camera, camera–IMU, IMU–IMU and laser–IMU calibration. The analysed methods allow the full calibration of a sensory system composed of lasers, cameras and IMUs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

2020

Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning

Autores
Luis, N; Pereira, T; Fern?ndez, S; Moreira, A; Borrajo, D; Veloso, M;

Publicação
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning. © 2019, The Author(s).

Teses
supervisionadas

2020

Autonomous Wheelchair to support Patients of Hospital Services

Autor
André Rodrigues Baltazar

Instituição
UP-FEUP

2020

Smart Tools for Agriculture Robotics

Autor
André Rodrigues Baltazar

Instituição
UP-FEUP

2020

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

Autor
Sandro Augusto Costa Magalhães

Instituição
UP-FEUP

2020

Odometry and Extrinsic Sensor Calibration on Mobile Robots

Autor
Ricardo Barbosa Sousa

Instituição
UP-FEUP

2020

Trustable Intelligent Decision Support for Enhancing Indusrial Digital Twins

Autor
Flávia Georgina da Silva Pires

Instituição
UP-FEUP