Abstract
Three-dimensional scanning is a task of great importance for our modern society and has brought significant advances in the precision of material inventory. These sensors map the material surface continuously, allowing real-time inventory monitoring. Most technologies are expensive because this process is complex, and even inexpensive ones are considerate smart investments for the average user. Therefore, this work presents the simulation of a low-cost time-of-flight based 3D scanning system that performs the volume estimation of an object-filled indoor space after a voxelization process. The system consists of a 2D LIDAR scanner that performs an azimuthal scan of 180. through its rotating platform and a motor that rotates the platform in angle inclination.

Abstract
The experimental component is an essential method in Engineering education. Sometimes the availability of laboratories and components is compromised, and the COVID19 pandemic worsened the situation. Resorting to an accurate simulation seems to help this process by allowing students to develop the work, program, test, and validate it. Moreover, it lowers the development time and cost of the prototyping stages of a robotics project. As a multidisciplinary area, robotics requires simulation environments with essential characteristics, such as dynamics, connection to hardware (embedded systems), and other applications. Thus, this paper presents the Simulation environment of SimTwo, emphasizing previous publications with models of sensors, actuators, and simulation scenes. The simulator can be used for free, and the source code is available to the community. Proposed scenes and examples can inspire the development of other simulation scenes to be used in electrical and mechanical Engineering projects.

Abstract

Abstract
The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an ?-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.

Abstract
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.

Abstract
The proposed work was developed as part of the SmartHealth project, which aims to advance upper body rehabilitation by granting a robotic alternative to reduce the limitations of physical therapy while conferring more intensive and personalized therapy sessions for patients. The use of robots permits therapists to be relieved of laborious and repetitive tasks while supplying feedback for patients and physiotherapists through automatic recordings. The proposed strategy is to develop new python-based software that controls the robot, collects the patient's forces and muscle activity in real-time, and stores them for future analysis while providing visual feedback, thus allowing session optimization. These features permit the physiotherapist to objectively perceive the patient's performance during exercise. This solution is implemented in robots already commercialized in the industrial field. These kinds of robots are generally mass-produced in production lines at a relatively low cost and with great flexibility.