Percorrer por autor "Costa, Carlos M."
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- 2D cloud template matching - a comparison between iterative closest point and perfect matchPublication . Sobreira, Héber; Rocha, Luís Freitas; Costa, Carlos M.; Lima, José; Costa, Paulo Gomes da; Moreira, António Paulo G. M.Self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to algorithms accuracy, robustness and computational efficiency. In this paper we present the comparison of two of the most used map-matching algorithm, which are the Iterative Closest Point and the Perfect Match. This category of algorithms are normally applied in localization based on natural landmarks. They were compared using an extensive collection of metrics, such as accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to outliers in the robots sensors data. The test results were performed in both simulated and real world environments.
- A LoRaWAN IoT system for smart agriculture for vine water status determinationPublication . Valente, António; Costa, Carlos M.; Pereira, Leonor Sousa; Soares, Bruno; Lima, José; Soares, SalvianoIn view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.
- Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transformPublication . Sobreira, Héber; Costa, Carlos M.; Sousa, Ivo; Rocha, Luís Freitas; Lima, José; Farias, P.C.M.A.; Costa, Paulo Gomes da; Moreira, António Paulo G. M.The self-localization of mobile robots is one of the most fundamental problems in the robotics navigation eld. It is a complex and challenging issue due to the hard requirements that autonomous mobile vehicles are subject to, particularly with regard to the algorithms accuracy, robustness and computational e ciency. In this paper, we present a comparison of the three most used map-matching algorithms for robot self-localization based on natural landmarks, namely our implementation of the Perfect Match (PM) and the Iterative Closest Point (ICP) along with the Normal Distribution Transform (NDT) available in the Point Cloud Library (PCL). Regarding the ICP algorithm, we introduce in this paper a new methodology for performing correspondence estimation using lookup tables that was inspired in the PM approach. 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 used in the PCL implementation and allowed the ICP algorithm to perform point cloud registration 5 to 9 times faster. For the purpose of comparing the presented algorithms we have considered a set of representative metrics, such as the pose estimation accuracy, the computational e ciency, the convergence speed, the maximum admissible initialization error and the robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset that contains several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article, showing its advantage for real-time embedded systems with limited computing power which require accurate pose estimation and fast reaction times when the robot is navigating at high speeds.
- Relationship Between Quality of Life, Level of Physical Activity, Physical Fitness, and Body Composition on the Academic Performance of High School Students in an Integrated Educational SystemPublication . Gazolla, Jeann C.; Ferreira-Júnior, João; Encarnação, Samuel; Schneider, André; Monteiro, António M.; Teixeira, José Eduardo; Forte, Pedro; Oliveira, João P.; Borba, Diego; Costa, Carlos M.; Vieira, Carlos A.Adolescence is a critical period for the development of physical and cognitive health. Understanding how lifestyle and physical health parameters relate to academic performance and quality of life may inform school-based interventions. Purpose: This study aimed to evaluate the relationship between physical activity level (PAL), quality of life (QoL), physical fitness (PF), strength, speed and agility, body composition, and academic performance (AP) in high school students. Research Design: A cross-sectional, correlational study using multiple linear regression models to assess predictive relationships. Study Sample: 365 students (aged 16.93 ± 0.94 years) participated in the study. Data Collection and Analysis: Evaluations included Body Mass Index (BMI); PAL; QoL; PF (handgrip strength, countermovement vertical jump, and agility); and AP. A multiple linear regression was conducted using AP as the dependent variable, with BMI, jump performance, agility, handgrip strength, and PAL scores as predictors. Five additional multiple linear regressions were performed, each with a QoL domain as the dependent variable, and the same set of predictors as in the AP model. Participants’ age and sex were included as covariates in all models. Results: Significant predictive capacity was observed for AP (F = 2.22, p = .028, R = 0.31, R2 = 0.093) and two QoL domains: physical health (F = 2.32, p = .021, R = 0.28, R2 = 0.079) and psychological health (F = 2.32 and p = .021, R = 0.28, R2 = 0.079); however, with weak correlation coefficients (0.2 ≤ R <0.4). Only jump performance and age significantly affected the AP model (β = 0.038, p = .014) and the psychological health domain model (β = 0.48, p = .018). Conclusions: The predictors explained 9.3% of the variance in AP and 7.9% of the variance in physical health and psychological health in QoL domains, suggesting that additional factors (e.g., socioeconomic status, dietary habits) may play a role. The findings highlight the importance of multifactorial approaches in future research.
- Viticulture under climate change: a case study on a water scarcity modelPublication . Pereira, Leonor; Valente, António; Soares, Bruno; Costa, Carlos M.; Soares, Salviano; Lima, José; Gonçalves, IgorChanges in climatic patterns hinder the prediction of water availability, being imperative to develop new strategies to optimise water management in the agricultural sector. A multi-sensor network is being developed by ADVID/CoLAB VINES&WINES and University of Trás-os-Montes and Alto Douro (UTAD), aiming to determine water stress in vineyards, as a Decision Support System (DSS) for winegrowers. Remote wireless data transmission through LoRaWAN technology, will allow the development of a Machine Learning based model for water stress mapping. Measured parameters include soil, plant, and atmosphere data, given the importance of soil-plant-atmosphere continnum when evaluating water status. The pilot is installed in a commercial vineyard in the Douro Demarcated Region (DDR), and different sensor's modules were distributed spatially in the parcel. Lower cost and higher range than WiFi or Bluetooth, LoRaWAN are especially important for applications in remote areas, where mobile networks have little coverage, allowing to benefit a larger number of producers. While overcoming the constraints of the current monitoring method (Scholander pressure bomb), this system will allow remote and continuous water monitoring, assisting the producer in decision making. Altogether, this solution will contribute to better management of water resources, as well to the sustainability and competitiveness of farms.