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Detalhes

Detalhes

  • Nome

    Paulo José Costa
  • Cargo

    Investigador Sénior
  • Desde

    01 junho 2009
010
Publicações

2024

Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data

Autores
Brito, T; Pereira, AI; Costa, P; Lima, J;

Publicação
OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, PT II, OL2A 2023

Abstract
Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

2024

Energy Efficiency Analysis of Differential and Omnidirectional Robotic Platforms: A Comparative Study

Autores
Chellal, AA; Braun, J; Bonzatto, L Jr; Faria, M; Kalbermatter, RB; Gonçalves, J; Costa, P; Lima, J;

Publicação
SYNERGETIC COOPERATION BETWEEN ROBOTS AND HUMANS, VOL 1, CLAWAR 2023

Abstract
As robots have limited power sources. Energy optimization is essential to ensure an extension for their operating periods without needing to be recharged, thus maximizing their uptime and minimizing their running costs. This paper compares the energy consumption of different mobile robotic platforms, including differential, omnidirectional 3-wheel, omnidirectional 4-wheel, and Mecanum platforms. The comparison is based on the RobotAtFactory 4.0 competition that typically takes place during the Portuguese Robotics Open. The energy consumption from the batteries for each platform is recorded and compared. The experiments were conducted in a validated simulation environment with dynamic and friction models to ensure that the platforms operated at similar speeds and accelerations and through a 5200 mAh battery simulation. Overall, this study provides valuable information on the energy consumption of different mobile robotic platforms. Among other findings, differential robots are the most energy-efficient robots, while 4-wheel omnidirectional robots may offer a good balance between energy efficiency and maneuverability.

2024

Design and Development of an Omnidirectional Mecanum Platform for the RobotAtFactory 4.0 Competition

Autores
Braun, J; Baidi, K; Bonzatto, L; Berger, G; Pinto, M; Kalbermatter, RB; Klein, L; Grilo, V; Pereira, AI; Costa, P; Lima, J;

Publicação
SYNERGETIC COOPERATION BETWEEN ROBOTS AND HUMANS, VOL 1, CLAWAR 2023

Abstract
Robotics competitions are highly strategic tools to engage and motivate students, cultivating their curiosity and enthusiasm for technology and robotics. These competitions encompass various disciplines, such as programming, electronics, control systems, and prototyping, often beginning with developing a mobile platform. This paper focuses on designing and implementing an omnidirectional mecanum platform, encompassing aspects of mechatronics, mechanics, electronics, kinematics models, and control. Additionally, a simulation model is introduced and compared with the physical robot, providing a means to validate the proposed platform.

2024

A Comparison of PID Controller Architectures Applied in Autonomous UAV Follow up of UGV

Autores
Bonzatto, L Jr; Berger, GS; Braun, J; Pinto, MF; dos Santos, MF; Junior, AO; Nowakowski, M; Costa, P; Wehrmeister, MA; Lima, J;

Publicação
ROBOT 2023: SIXTH IBERIAN ROBOTICS CONFERENCE, VOL 2

Abstract
The cooperation between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has brought new perspectives and effectiveness to production and monitoring processes. In this sense, tracking moving targets in heterogeneous systems involves coordination, formation, and positioning systems between UGVs and UAVs. This article presents a Proportional-Integral-Derivative (PID) control strategy for tracking moving target operations, considering an operating environment between a multirotor UAV and an indoor UGV. Different PID architectures are developed and compared to each other in the Gazebo simulator, whose objective is to analyze the control performance of the UAV when used to track the ground robot based on the identification of the ArUco fiducial marker. Computer vision techniques based on the Robot Operating System (ROS) are integrated into the UAV's tracking system to provide a visual reference for the aircraft's navigation system. The results of this study indicate that the PD, Cascade, and Parallel controllers showed similar performance in both trajectories tested, with the Parallel controller showing a slight advantage in terms of mean error and standard deviation, suggesting its suitability for applications that prioritize precision and stability.

2024

Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition

Autores
Klein, LC; Mendes, J; Braun, J; Martins, FN; de Oliveira, AS; Costa, P; Wörtche, H; Lima, J;

Publicação
OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, PT II, OL2A 2023

Abstract
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

Teses
supervisionadas

2023

Human-Motion Aware Collision Handling and Avoidance during Motion Planning

Autor
Victor Cambraia Nogueira de Oliveira

Instituição
UP-FEUP

2023

Data fusion and smart sensors for advanced autonomous robotics

Autor
João Afonso Braun Neto

Instituição
UP-FEUP

2023

Legged-Wheeled Robot Locomotion with Variable Stiffness Joints

Autor
João Pedro Ribeiro Moreira

Instituição
UP-FEUP

2023

Localization of Mobile Robots Using Optical Flow Sensors and Sensor Fusion

Autor
Eduardo Passos Vila-Chã

Instituição
UP-FEUP

2023

Protótipos de robots móveis com diferentes configurações de locomoção

Autor
Gonçalo Rendeiro Brochado Garganta

Instituição
UP-FEUP