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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

Details

045
Publications

2021

Robust human position estimation in cooperative robotic cells

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

Publication
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

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

Publication
Robotics and Computer-Integrated Manufacturing

Abstract

2021

Autonomous wheelchair for patient’s transportation on healthcare institutions

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

Publication
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

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

Publication
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

Authors
Luis, N; Pereira, T; Fern?ndez, 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).

Supervised
thesis

2020

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

Author
Sandro Augusto Costa Magalhães

Institution
UP-FEUP

2020

Trustable Intelligent Decision Support for Enhancing Indusrial Digital Twins

Author
Flávia Georgina da Silva Pires

Institution
UP-FEUP

2020

Odometry and Extrinsic Sensor Calibration on Mobile Robots

Author
Ricardo Barbosa Sousa

Institution
UP-FEUP

2020

Multi-sensor approach for Power Lines Inspection with an Unmanned Aerial Vehicle

Author
Tiago André Miranda dos Santos

Institution
UP-FEUP

2020

Autonomous Wheelchair to support Patients of Hospital Services

Author
André Rodrigues Baltazar

Institution
UP-FEUP