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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.

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Details

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034
Publications

2020

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

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

Publication
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

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

Publication
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.

2019

Preface

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

Publication
Advances in Intelligent Systems and Computing

Abstract

2019

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

Authors
Sobreira, H; Costa, CM; Sousa, I; Rocha, L; Lima, J; Farias, PCMA; Costa, P; Paulo Moreira, AP;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

Optimal Perception Planning with Informed Heuristics Constructed from Visibility Maps

Authors
Pereira, T; Moreira, A; Veloso, M;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time. © 2018 Springer Science+Business Media B.V., part of Springer Nature

Supervised
thesis

2019

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

Author
Ahmad Safaee

Institution
UP-FEUP

2019

A User-centric Approach to Locomotion Supporting Systems

Author
Diana Alves Lobo Guimarães

Institution
UP-FEUP

2019

Optimal Automatic Path Planning and Design for High Redundancy Robotic Systems

Author
Pedro Miguel Santos Tavares

Institution
UP-FEUP

2019

Robot-Dependent Maps for Coverage and Perception Task Planning

Author
Tiago Raúl de Sousa Pereira

Institution
UP-FEUP

2018

Controlo das trajetórias de um robô móvel de alto desempenho

Author
Sandro Augusto Costa Magalhães

Institution
UP-FEUP