This article presents a new robust formation control approach for a system of multiple Quadcopter Unmanned Aerial Vehicle (QUAV) in complex and challenging flight scenarios with large lumped uncertainties. The aim is to achieve fast convergence to the desired formation pattern, high tracking accuracy, and solid robustness. To this end, a novel finite-time nonlinear control strategy, Adaptive Backstepping Recursive Integral Non-singular Fast Terminal Sliding Mode Control (ABRINFTSMC), is designed. The formation control system of each QUAV, based on ABRINFTSMC, is divided into an underactuated position subsystem for the outer loop and a fully actuated attitude subsystem for the inner loop. A Robust Finite-Time Distributed Consensus Formation Control Protocol (RFDCFCP) is developed for the outer loop to ensure the convergence of the aircraft to the desired formation pattern in a finite time and maintain its alignment throughout the flight mission. For the inner loop, a Robust Finite-Time Attitude Stabilization Control (RFASC) is developed to allow the aircraft to track the desired attitude generated by the outer loop in a finite time. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory to ensure convergence of all formation tracking errors to the origin in finite time. Finally, numerical simulations are performed under different scenarios to validate the performance of the proposed method, and a comprehensive comparative study with recent formation control approaches is carried out. The results confirm that the proposed formation control exhibits superior performance regarding fast convergence, tracking accuracy, and robustness, crucial features for multi-QUAV system flight formation.

Inertial navigation systems (INS) are widely used in commercial aviation, maritime navigation, and unmanned vehicle guidance. However, these systems are often sensitive, costly, and challenging to access. To address these limitations, an open-source, low-cost platform named INS OpenNavSense has been developed. This platform is built using FreeRTOS, an open-source real-time operating system (RTOS) that enables the microcontroller to run parallel individual threads (tasks), providing a practical and effective tool for implementing estimation algorithms that compensate for the use of low-cost microelectromechanical systems (MEMS) sensors instead of high-end sensors in professional INS. The main contribution of this work is the introduction of a FreeRTOS-based platform, which facilitates independent management of computational and processing tasks. The platform incorporates accelerometers, gyroscopes, magnetometers, Global Positioning System (GPS) module, and barometer sensors. Sensor data is calibrated and filtered to enhance accuracy, offering researchers a robust and reliable tool for testing their estimation algorithms. To validate this platform, the open-source Mahony library was used for attitude and heading reference system estimation, demonstrating the types of algorithms that can be tested. Tests were conducted with a drone carrying the platform as payload, and results from this low-cost INS were compared to the drone’s INS, showing both similarity and viability as a development platform.

The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems.

This paper presents a methodology for detecting the pest Aleurothrixus floccosus in citrus crops in Pedregal de Arequipa, Peru. The study employs simple random sampling during image collection to minimize bias, alternating and extracting leaves from different citrus trees. Image processing techniques, including noise reduction, edge smoothing, and segmentation, are applied for pest detection. Machine learning algorithms are used to classify the images, culminating in a robust detection methodology. A dataset of 1200 images was analyzed during the study.

Robotics and LiDAR technology stand as a crucial cornerstone for the development of cutting-edge three-dimensional mapping systems. This study represents a significant advancement by addressing the development of an initial approach for a three-dimensional mapping system, utilizing a unique LiDAR translational mechanism. In pursuit of this objective, a comprehensive review of works exclusively dedicated to mechanisms employing two-dimensional LiDAR has been conducted. This selective approach results in a comprehensive understanding of the mechanism used for three-dimensional reconstruction and lays the groundwork for future endeavors. Furthermore, a robotic prototype has been implemented using the Robot Operating System (ROS), serving as an accessible tool for implementing our initial approach and engaging new researchers from our university in the application of robotics for three-dimensional reconstruction through LiDAR technology. The validation of our study is conducted through tests in both open and closed environments, revealing high data resolution and a correlation of over 98% with the real environment. The study suggests further research based on the identified errors and introduces new challenges for developing robust prototypes capable of handling changes in a robot's attitude.