Industrial robots play a crucial role in a wide range of industrial processes, and due to the complexity of the work environment in which these systems are deployed, more robust and accurate control methods are required. Deep reinforcement learning (DRL) emerges as a comprehensive approach that does not require an initial source of structured data for its learning process. Instead, DRL generates its own data based on its experiences within a work environment. To generate its own data, DRL requires integration with virtualized environments, provided by simulators. These tools must include scenarios in industrial contexts and allow integration with ML tools, among other capabilities. Currently, there are several platforms that support the simulation of various scenarios and the generation of synthetic data, facilitating the development of end-to-end systems based on artificial intelligence, such as DRL. This article presents an extensive review of software tools applied in DRL-based control systems for robotic manipulators. The selection of these tools is based on their efficiency, scalability, and compatibility with contemporary industrial standards, offering insights into their practical application in real-world scenarios. Consequently, this paper establishes a complete framework for designing and developing control systems for robotic manipulators using end-to-end DRL. This framework outlines in detail the tools, including simulators, APIs, libraries, and methods, and their interactions with each other. Additionally, it discusses the practical implications of this framework, highlighting its potential applications in industry and addressing some of the challenges and limitations encountered in applying DRL to complex robotic systems.

The present work is part of the “Hand of Hope” project, which seeks to develop low-cost robotic prostheses with the aim of contributing to the social and labor inclusion of people with motor disabilities of their upper extremities. The specific objective to address in this work is the design and development of the system architecture to recognition of EMG (Electromyography) signal patterns, the purpose is to control the functions of objects handling by the robotic prosthesis. There are proposed methods to the recognition of EMG signal patterns; however it defined using a Feedforward-backpropagation Artificial Neural Network (ANN) because have high success rate (Sr) using the least amount of channels, can be supported on platforms on-line and real-time; and moreover in order to improve the ANN-Sr values, it was used as input the envelope of the EMG signal instead of the original signal. For the performance evaluation 20 assessments for each of the four EMG signal patterns (relaxed hand/cylindrical grip, pinch grip, thumb adduction, and index finger extended) were performed, from the results it is observed that the average success rate is equal to 95%.

This article describes the detection of the characters of the license plate through of computer vision techniques: such as cascade of classifiers based in sobel algorithm, analysis of peaks and valleys, and support vector machines; the search for the region of the plate begins by detecting vehicles, then character segmentation and concludes with the recognition of these. The system was tested in different scenarios such as: different lighting provided by the day or night and weather changes. The average time to execute these phases it was 434 ms.

The main contribution of this paper is that a novel method of verification of the statistical performance of electronic circuits that have been specifically designed to analyze the power quality is proposed. The method is based on the Wilcoxon signed-rank test and on the Levene test, which are nonparametric statistical inference techniques, and it establishes the relation that exists between the measurement results obtained using a reference instrument and the measurement results obtained using the electronic circuit under test. In addition, the method says whether the performance of the device under test differs significantly from what would be expected. Finally, in order to show the feasibility of the proposed method, a prototype circuit was designed and a practical application of the method is shown. The main idea of the proposed method can also be used to analyze the performance of a wide range of circuits used in different industrial applications.http://www.sciencedirect.com/science/article/pii/S026322411630353