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Sobre
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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

037
Publicações

2020

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

Autores
Luis, N; Pereira, T; Fernandez, 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).

2020

Optimal automatic path planner and design for high redundancy robotic systems

Autores
Tavares, P; Marques, D; Malaca, P; Veiga, G; Costa, P; Moreira, AP;

Publicação
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION

Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

2020

Driverless Wheelchair for Patient's On-Demand Transportation in Hospital Environment*

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

Publicação
2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020, Ponta Delgada, Portugal, April 15-17, 2020

Abstract
The transport of patients from the inpatient service to the operating room is a recurrent task in the hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented the design of a driverless wheelchair under development capable of providing an on-demand mobility service to hospitals. The proposed wheelchair 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 destination. © 2020 IEEE.

2020

Evolution of odometry calibration methods for ground mobile robots

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

Publicação
2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020

Abstract
Localisation is a critical problem in ground mobile robots. For dead reckoning, odometry is usually used. A disadvantage of using it alone is unbounded error accumulation. So, odometry calibration is critical in reducing error propagation. This paper presents an analysis of the developments and advances of systematic methods for odometry calibration. Four steering geometries were analysed, namely differential drive, Ackerman, tricycle and omnidirectional. It highlights the advances made on this field and covers the methods since UMBmark was proposed. The points of analysis are the techniques and test paths used, errors considered in calibration, and experiments made to validate each method. It was obtained fifteen methods for differential drive, three for Ackerman, two for tricycle, and three for the omnidirectional steering geometry. A disparity was noted, compared with the real utilisation, between the number of published works addressing differential drive and tricycle/Ackerman. Still, odometry continues evolving since UMBmark was proposed. © 2020 IEEE.

2019

Preface

Autores
Costa, AP; Moreira, A; Reis, LP;

Publicação
Advances in Intelligent Systems and Computing

Abstract

Teses
supervisionadas

2019

Optimal Automatic Path Planning and Design for High Redundancy Robotic Systems

Autor
Pedro Miguel Santos Tavares

Instituição
UP-FEUP

2019

Robot-Dependent Maps for Coverage and Perception Task Planning

Autor
Tiago Raúl de Sousa Pereira

Instituição
UP-FEUP

2019

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

Autor
Ahmad Safaee

Instituição
UP-FEUP

2019

A User-centric Approach to Locomotion Supporting Systems

Autor
Diana Alves Lobo Guimarães

Instituição
UP-FEUP

2018

Localização e Navegação de AGVs Industriais

Autor
Emanuel Pereira Teixeira

Instituição
UP-FEUP