Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

This paper presents the use of Support Vector Machines (SVM) methodology for fault detection and diagnosis. Two approaches are addressed: the SVM for classification (Support Vector Classification – SVC) and SVM for regression (Support Vector Regression – SVR). A comparison was made between the two techniques through the study of a reactor of cyclopentenol production. In the case studied, different fault scenarios were introduced and it was evaluated which technique was able to detect and diagnose them. Finally, a comparison was made between the fault detection methodologies based on SVM and Dynamic Principal Component Analysis (DPCA) based detection techniques for a jacketed CSTR.

Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments.