Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios.

The exposure of transmission lines in close proximity to coastal or industrial environments to high levels of pollution from chemical substances and salts is a factor that can degrade the performance of electrical systems. Given this scenario, the washing of insulators is an essential measure to mitigate the adverse effects of these pollutants. However, the process is often risky considering that technicians work in close proximity to live equipment. To address this safety concern, a drone-robot for automatic cleaning of insulator chain is proposed. This equipment, adapted with a high-pressure water pump, was designed to perform the task of cleaning insulators on live lines, thus providing a superior level of safety for technical personnel. The drone is equipped with an RGBD camera and an onboard computer, enabling tracking of the target insulator for cleaning using artificial intelligence. The movement of the drone is determined by the generation of setpoints for the flight controller based on this tracking. The results were obtained from experimental tests conducted on high voltage power transmission lines, demonstrating consistent performance in terms of flight stability and cleaning efficiency. These findings provide strong evidence of the operational robustness and practical feasibility of the proposed drone-robot system in high-risk electrical environments.

Este trabalho objetiva apresentar o COPILOTS (BasiC OPtImaLCOnTrol Solver), um pacote proposto para resolução de Problemas de Controle Ótimo (PCOs) baseado nos Métodos Diretos, de fácil utilização e ideal para usuários com pouca ou nenhuma experiência na resolução de PCOs. Em linhas gerais, a ferramenta proposta realiza a discretização dos controles via colocação trapezoidal ou colocação Hermite-Simpson, ao passo que empregam-se polinômios cúbicos na interpolação dos estados. Com efeito, o problema original (algébrico-diferencial) é transformado em um puramente algébrico, que é resolvido considerando o SQP (Sequential Quadratic Programming). Para fins de validação, o COPILOTS foi empregado na determinação da trajetória de tempo mínimo a ser percorrida por um veículo na execução de uma manobra de estacionamento paralelo e um novo algoritmo para inicialização dos estados e controles foi proposto de forma a minimizar o esforço computacional despendido. Os resultados demonstram que o pacote proposto configura-se como uma alternativa para a resolução de PCOs.

O monitoramento de V´alvulas de Controle de Fluxo (ICVs) em poc¸os de petr´oleo ´e fundamental para garantir a eficiˆencia operacional, mas ´e dificultado pela escassez de dados de falha. Este artigo apresenta uma metodologia baseada em aprendizado n˜ao supervisionado para detecc¸ ˜ao de anomalias em ICVs, superando a necessidade de dados rotulados. A abordagem utiliza uma rede neural autoencoder, que aprende a reconstruir dados de operac¸ ˜ao normal com alta fidelidade. Anomalias s˜ao ent˜ao identificadas quando o erro de reconstruc¸ ˜ao dos dados excede um limiar definido. Para validac¸ ˜ao, anomalias s˜ao criadas artificialmente no conjunto de teste, introduzindo desvios estatisticamente significativos. Os resultados mostram que o erro de reconstruc¸ ˜ao para as amostras anˆomalas ´e significativamente maior do que para as amostras normais. Isso demonstra que a abordagem n˜ao supervisionada ´e uma estrat´egia eficaz e robusta para o monitoramento de ICVs, apresentando uma alternativa vi´avel aos m´etodos supervisionados tradicionais.

Electrical power systems are critical for socioeconomic development, and marker balls are essential for preventing collisions with low-flying aircraft. Traditionally, the installation of such markers is performed manually by technical staff, exposing them to risks such as falls, electric shocks, and physical injuries. Drones, especially when integrated with modular robots, can efficiently reach installation points along transmission lines, overcoming the limitations of manual operations. Existing drone-based solutions rely on manual control, which requires highly skilled pilots and provides limited precision, and, to date, no studies have investigated autonomous flights for this task. To address these challenges, this paper proposes an autonomous drone-robot control system for marker ball installation. The proposed algorithm combines depth image processing based on filtering and line fitting with RGB color based processing, enabling automatic landing of the robot on the power cable. More precisely, an onboard computer processes the captured images and generates setpoints sent to the flight controller, responsible for controlling the drone’s movement. Experiments conducted in a controlled environment, with an average installation time of 43 s, demonstrated the effectiveness of the proposed system. The results show that autonomous control reduces the need for pilot skill and enables safe and faster installation of marker balls.