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About

Born at Porto, Portugal, November 7, 1962, graduated with a degree in Electrical Engineering  from the University of Porto in 1986. He then pursued graduate studies at the University of Porto, completing a M.Sc. degree in Electrical Engineering - Systems in 1991 and a Ph.D. degree in Electrical Engineering in 1998. From1986 to 1998 he also worked as an assistant lecturer in the Electrical Engineering Department of the University of Porto. He is currently an Associated Professor in Electrical Engineering, developing his research within the Robotic and Intelligent Systems Centre of INESC TEC (Centre Coordinator). His main research areas are Process Control and Robotics.

Interest
Topics
Details

Details

017
Publications

2018

Landmark detection for docking tasks

Authors
Ferreira, F; Sobreira, H; Veiga, G; Moreira, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
For docking manoeuvres, the detection of the objects to dock needs to be precise as the minimum deviation from the objective may lead to the failure of this task. The objective of this article is to test possible ways to detect a landmark using a laser rangefinder for docking manoeuvres. We will test a beacon-based localisation algorithm and an algorithm based on natural landmarks already implemented, however, we will apply modifications to such methods. To verify the possibility of docking using these methods, we will conduct experiments with a real robot. © Springer International Publishing AG 2018.

2018

Robot localization system in a hard outdoor environment

Authors
Conceição, T; dos Santos, FN; Costa, P; Moreira, AP;

Publication
Advances in Intelligent Systems and Computing

Abstract
Localization and mapping of autonomous robots in a hard and unstable environment (Steep Slope Vineyards) is a challenging research topic. Typically, the commonly used dead reckoning systems can fail due to the harsh conditions of the terrain and the Global Position System (GPS) accuracy can be considerably noisy or not always available. One solution is to use wireless sensors in a network as landmarks. This paper evaluates a ultra-wideband time-of-flight based technology (Pozyx), which can be used as cost-effective solution for application in agricultural robots that works in harsh environment. Moreover, this paper implements a Localization Extended Kalman Filter (EKF) that fuses odometry with the Pozyx Range measurements to increase the default Pozyx Algorithm accuracy. © Springer International Publishing AG 2018.

2018

Flexible work cell simulator using digital twin methodology for highly complex systems in industry 4.0

Authors
Tavares, P; Silva, JA; Costa, P; Veiga, G; Moreira, AP;

Publication
Advances in Intelligent Systems and Computing

Abstract
The continuous evolution in manufacturing processes has attracted substantial interest from both scientific and research community, as well as from industry. Despite the fact that streamline manufacturing relies on automation systems, most production lines within the industrial environment lack a flexible framework that allows for evaluation and optimisation of the manufacturing process. Consequently, the development of a generic simulators able to mimic any given workflow represent a promising approach within the manufacturing industry. Recently the concept of digital twin methodology has been introduced to mimic the real world through a virtual substitute, such as, a simulator. In this paper, a solution capable of representing any industrial work cell and its properties is presented. Here we describe the key stages of such solution which has enough flexibility to be applied to different working scenarios commonly found in industrial environment. © 2018, Springer International Publishing AG.

2018

Heterogeneous Multi-Agent Planning Using Actuation Maps

Authors
Pereira, T; Luis, N; Moreira, A; Borrajo, D; Veloso, M; Fernandez, S;

Publication
2018 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

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 that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing 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 the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

2018

Soft computing optimization for the biomass supply chain operational planning

Authors
Pinho, TM; Coelho, JP; Veiga, G; Moreira, AP; Boaventura Cunha, J;

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
Supply chains are complex interdependent structures in which tasks' accomplishment is the result of a compromise between all the entities involved. This complexity is particularly pronounced when dealing with chipping and transportation tasks within a forest-based biomass energy production supply chain. The logistic costs involved are significant and the number of network nodes are usually in a considerable number. For this reason, efficient optimization tools should be used in order to derive cost effective scheduling. In this work, soft computing optimization tools, namely genetic algorithms (GA) and particle swarm optimization (PSO), are integrated within a discrete event simulation model to define the vehicles operational schedule in a typical forest biomass supply chain. The presented simulation results show the proposed methodology effectiveness in dealing with the addressed systems. © 2018 IEEE.

Supervised
thesis

2017

Grasp planning for handoff between robotic manipulators

Author
David Miguel Ribeiro de Sousa

Institution
UP-FEUP

2017

Navegação e controlo de robôs móveis com atrelagem de reboques automática

Author
Francisco Abílio Rodrigues Guerra Ferreira

Institution
UP-FEUP

2017

Boccia Game simulator - Applications for training cerebral palsy patients

Author
José Diogo Machado Ribeiro

Institution
UP-FEUP

2017

Implementação em Chão de Fábrica do Sistema de Corte de Rolos Calandrados

Author
Paulo Miranda Rebelo

Institution
UP-FEUP

2017

Fusão sensorial e cooperação em equipas de robôs móveis

Author
Pedro Miguel da Silva Rocha Relvas

Institution
UP-FEUP