Percorrer por autor "Santos, Murillo Ferreira dos"
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- A Comparison of PID Controller Architectures Applied in Autonomous UAV Follow up of UGVPublication . Bonzatto Junior, Luciano; Berger, Guido S.; Braun, João A.; Pinto, Milena F.; Santos, Murillo Ferreira dos; Oliveira Júnior, Alexandre de; Nowakowski, Marek; Costa, Paulo; Wehrmeister, Marco A.; Lima, JoséThe cooperation between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has brought new perspectives and e!ectiveness 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. Di!erent 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.
- Advancing Sustainability and Productivity: The Role of Precision Agriculture in Vineyards and Olive GrovesPublication . Fernandes, Fernanda Mara; Santos, Murillo Ferreira dos; Morais, Maurício Herche Fófano de; Lima, José; Pereira, Ana I.; Mercorelli, PaoloPrecision agriculture has emerged as a vital approach to modern agricultural management, addressing the dual challenge of increasing food production while preserving the environment. Its importance lies in its ability to leverage advanced technologies to optimize productivity, reduce waste, and ensure sustainability, particularly in high-value crops such as vineyards and olive groves. This study explores the application of precision agriculture tools, such as sensors, drones, geolocation systems, and data analytics, in these crops to enhance productivity, improve product quality, and minimize environmental impact. In vineyards, precision viticulture focuses on managing spatial and temporal variability within plots to increase economic performance through higher productivity, superior fruit quality, and reduced production costs. The targeted application of inputs and precise management practices result in resource savings and uniform fruit quality, crucial for producing premium wines. Similarly, in olive groves, technologies enable effective plant health monitoring and early disease detection. At the same time, drones assist in evaluating plant vigor and planning optimal harvest times, ultimately maximizing yield and olive oil quality. By integrating traditional agricultural practices with modern technological advancements, this study anticipates a range of positive outcomes, including reduced resource waste, improved competitiveness in global markets, and strengthened sustainability in production networks. The findings underscore the transformative potential of precision agriculture, offering valuable insights into sustainable agricultural development and setting a pathway for further innovation in the sector.
- Attitude Determination and Control System Open-Source Simulator of a 1U CubeSat NanosatellitePublication . Resende, Túlio Santos; Silva, Josué Lima da; Peixoto, Alessandro Jacoud; Santos, Murillo Ferreira dos; Pereira, Ana I.; Lima, José; Morais, Maurício Herche Fófano deThis work developed an open-source educational simulator to evaluate control schemes for small CubeSat-class satellites of 1U size (1 unit). Attitude (orientation) estimation was achieved using the Three-Axis Attitude Determination (TRIAD) algorithm, commonly employed in competitions and real-world CubeSats. A simple Proportional (P) controller was designed to regulate the CubeSat's angular velocity. Additionally, for pointing control (line-of-sight orientation), a Proportional-Derivative (PD) controller was implemented. Numerical simulations were conducted to assess the performance of the CubeSat's Attitude Determination and Control Subsystem (ADCS).
- Control Allocation and Controller Tuning for an Over-Actuated Hexacopter Tilt-Rotor Applied for Precision AgriculturePublication . Libório, Leandro Oliveira; Pimentel, Gabriel Oliveira; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Morais, Maurício Herche Fófano de; Mercorelli, Paolo; Pereira, Ana I.This work presents the control allocation and tuning methodology for an over-actuated Hexacopter Tilt-Rotor (HTR) designed for precision agriculture applications. The HTR's innovative design includes two independently tiltable rotors, enhancing stability and forward velocity, making it suitable for low-altitude maneuvers in agricultural environments. The study focuses on the implementation of a cascade Proportional (P)-Proportional, Integral and Derivative (PID) control structure with Successive Loop Closure (SLC) and the application of an extended Fast Control Allocation (FCA) method to optimize actuator performance. The control gains were meticulously tuned to ensure stability and robustness across six degrees of freedom, achieving precise trajectory tracking and efficient resource use. Validation was conducted through simulations using Robot Operating System (ROS) and Gazebo, replicating realistic precision agriculture scenarios. Results demonstrate the efficacy of the proposed control strategies, highlighting their potential for real-world applications in crop monitoring, pest detection, and resource optimization. Future work includes physical implementation and integration with collaborative robotics.
- A deep learning approach for average height estimation in oak colony using rgb imagesPublication . Britto, Raphael Duarte; Mendes, João; Grilo, Vinicius; Castro, João Paulo; Santos, Murillo Ferreira dos; Castro, Marina; Pereira, Ana I.; Lima, JoséMany strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.
- A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision TechnologiesPublication . Morais, Maurício Herche Fófano de; Mendes, João; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Pereira, Ana I.This work presents an approach for detecting olive knot disease in olive trees, utilizing Computer Vision (CV), Unmanned Aerial Vehicle (UAV) based imagery, and Machine Learning (ML) within the context of Precision Agriculture (PA). The study focuses on applying the You Only Look Once (YOLO) deep learning architecture to develop a model capable of identifying trees affected by the disease with accuracy and speed. By integrating UAV technology with object detection algorithms, this approach enables real-time monitoring of olive plantations, supporting early detection and targeted interventions. This study emphasizes the potential of combining drone imaging and ML to drive sustainable and practical solutions in PA. Results show that this method can potentially improve crop management by reducing human labor and contributing to the enhancement of disease control strategies.