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About

About

Born at Porto, Portugal, April  6, 1973, received the M.Sc.in Electrical and Computer Engineering on Faculty of Engineering of University of Porto, Portugal in 1999. He obtained a Ph.D. in Electrical and Computer Engineering on Faculty of Engineering of University of Porto in the area of Control and Robotics, with the thesis “Planning Cooperative tasks and trajectories in Multiple Robots” in 2011. Presently he is a Professor at Computers and Electrical Engineering Department of the Oporto University. He is also a researcher in Robotic and Intelligent Systems of the INESC-TEC (Institute for Systems and Computer Engineering of Porto, Portugal). His research interests are in the ï¬�eld of robotics and automation: path planning, obstacle avoidance, simulation, navigation, manipulator, mobile manipulators. 

Interest
Topics
Details

Details

  • Name

    Pedro Gomes Costa
  • Role

    Senior Researcher
  • Since

    01st June 2009
011
Publications

2023

Modelling of a Vibration Robot Using Localization Ground Truth Assisted by ArUCo Markers

Authors
Matos, D; Lima, J; Rohrich, R; Oliveira, A; Valente, A; Costa, P; Costa, P;

Publication
ROBOTICS IN NATURAL SETTINGS, CLAWAR 2022

Abstract
Simulators have been increasingly used on development and tests on several areas. They allow to speed up the development without damage and no extra costs. On realistic simulators, where kinematics play an important role, the modelling process should be imported for each component to be accurately simulated. Some robots are not yet modelled, as for example the Monera. This paper presents a model of a small vibration robot (Monera) that is acquired in a developed test-bed. A localisation ground truth is used to acquire the position of the Monera with actuating it. Linear and angular speeds acquired from real experiments allow to validate the proposed methodology.

2023

Multi-robot Coordination for a Heterogeneous Fleet of Robots

Authors
Pereira, D; Matos, D; Rebelo, P; Ribeiro, F; Costa, P; Lima, J;

Publication
ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 2

Abstract
There is an increasing need for autonomous mobile robots (AMRs) in industrial environments. The capability of autonomous movement and transportation of items in industrial environments provides a significant increase in productivity and efficiency. This need, coupled with the possibility of controlling groups of heterogeneous robots, simultaneously addresses a wide range of tasks with different characteristics in the same environment, further increasing productivity and efficiency. This paper will present an implementation of a system capable of coordinating a fleet of heterogeneous robots with robustness. The implemented system must be able to plan a safe and efficient path for these different robots. To achieve this task, the TEA* (Time Enhanced A*) graph search algorithm will be used to coordinate the paths of the robots, along with a graph decomposition module that will be used to improve the efficiency and safety of this system. The project was implemented using the ROS framework and the Stage simulator. Results validate the proposed approach since the system was able to coordinate a fleet of robots in various different tests efficiently and safely, given the heterogeneity of the robots.

2023

Position Estimator for a Follow Line Robot: Comparison of Least Squares and Machine Learning Approaches

Authors
Matos, D; Mendes, J; Lima, J; Pereira, AI; Valente, A; Soares, S; Costa, P; Costa, P;

Publication
ROBOTICS IN NATURAL SETTINGS, CLAWAR 2022

Abstract
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

2022

Path Planning with Hybrid Maps for processing and memory usage optimisation

Authors
Santos, LC; Santos, FN; Aguiar, AS; Valente, A; Costa, P;

Publication
2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
Robotics will play an essential role in agriculture. Deploying agricultural robots on the farm is still a challenging task due to the terrain's irregularity and size. Optimal path planning solutions may fail in larger terrains due to memory requirements as the search space increases. This work presents a novel open-source solution called AgRob Topologic Path Planner, which is capable of performing path planning operations using a hybrid map with topological and metric representations. A local A* algorithm pre-plans and saves local paths in local metric maps, saving them into the topological structure. Then, a graph-based A* performs a global search in the topological map, using the saved local paths to provide the full trajectory. Our results demonstrate that this solution could handle large maps (5 hectares) using just 0.002 % of the search space required by a previous solution.

2022

Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey

Authors
Cordeiro, A; Rocha, LF; Costa, C; Costa, P; Silva, MF;

Publication
2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
Bin picking is a highly researched topic, due to the need for automated procedures in industrial environments. A general bin picking system requires a highly structured process, starting with data acquisition, and ending with pose estimation and grasping. A high number of bin picking problems are being presently solved, through deep learning networks, combined with distinct procedures. This study provides a comprehensive review of deep learning approaches, implemented in bin picking problems. Throughout the review are described several approaches and learning methods based on specific domains, such as gripper oriented and object oriented, as well as summarized several methodologies, in order to solve bin picking issues. Furthermore, are introduced current strategies used to simplify particular cases and at last, are presented peculiar means of detecting object poses.

Supervised
thesis

2022

Multi-AGV Coordination for Heterogeneous Robots

Author
Diogo André Silva Pereira

Institution
UP-FEUP

2022

O&M optimization for multi-asset offshore renewable energy parks

Author
Francisco José Vieira Parente

Institution
UP-FEUP

2022

Islanding Operation and Black Start Strategies for Multi-Microgrids using the Smart Transformer

Author
Mário Jorge Teixeira Couto

Institution
UP-FEUP

2022

Automatic recognition of criminals, victims, and illegal behaviour in videos

Author
Leonardo Gomes Capozzi

Institution
UP-FEUP

2021

Holistic performance and scalability analysis for large-scale distributed systems

Author
Francisco Nuno Teixeira Neves

Institution
UM