Smart homes, known for their capacity to facilitate seamless environmental control, have garnered increasing attention in recent years. In this paper the potential of Frequency Modulated Continuous Wave Radar for autonomous detection and classification of human movements within indoor environments is investigated. Data from ten subjects were employed to evaluate and distinguish the efficacy of four Machine Learning algorithms — K-Nearest Neighbor, Support Vector Machine, Linear Discriminant Analysis, and Random Forest — in classifying movements into the categories of “Stopped”, “Moving”, and “Working Out”. Each algorithm is compared, not only with performance metrics but also considerations of creation time, memory usage, and results variability are considered. The results, underscored by a 97.96% accuracy rate predominantly attained through the Random Forest algorithm, illustrate the practicality of employing these technologies for movement classification within smart homes. While the K-Nearest Neighbor algorithm consumed the least memory, Linear Discriminant Analysis proved to be the fastest, and the Support Vector Machine exhibited the least favorable performance in terms of both accuracy and resource efficiency.

The world of robotics is in constant evolution, trying to find new solutions to improve on top of the current technology and to overcome the current industrial pitfalls. To date, one of the key intelligent robotics components, path planning algorithms, lack flexibility when considering dynamic constraints on the surrounding work cell. This is mainly related to the large amount of time required to generate safe collision-free paths for high redundancy systems. Furthermore, and despite the already known benefits, the adoption of CPU/GPU parallel solutions is still lacking in the robotic field. This work presents a software solution able of connecting the path planning algorithms with parallel computing tools, reducing the time needed to generate a safe path. The output of this work is the validation for the introduction of intelligent parallel solutions in the robotic sector.

Welding physics is complex, and therefore the welding parametrization is time-consuming. In manual welding, the "hand", the experience, and the best sensor of all (the eyes) can compensate for the difficulties in finding the right settings (welding parameters, robot posture, speed,...) for a specific weld seam. In robotic welding the robotic arm and the sensors are limited, and the parametrization time escalates. This work aims to develop a flexible welding robotized system, through the introduction of (knowledge-based) decision support for welding parametrization in an advanced robotic work cell, in combination with advanced (collision-free) offline programming and advanced sensing. By selecting a specific application area, structural steel, this work will reduce the degree of complexity during the development, paving the way for the introduction of knowledge-based welding in the robotic arc welding sector.