The field of assistive robotics is experiencing rapid growth due to recent advancements in computing, control systems, and instrumentation. Physiotherapeutic methods, such as gait rehabilitation and learning, are frequently deployed to teach and aid individuals with disabilities, particularly children with cerebral palsy (CP), as enhancing mobility is crucial for their overall health and future independence. These methods can be enhanced through assistive technologies, which can streamline treatment, reduce the physical burden on physiotherapists, boost the efficiency of therapeutic techniques, and improve the precision and consistency of therapeutic movements. This work introduces the modeling, control, and prototyping of an omnidirectional mobile platform designed for assistive robotics applications, with the goal of expanding movement possibilities during rehabilitation. The paper addresses issues like the impact of holonomic approximations on dynamic modeling, proposes a control architecture, and presents a cost-effective generalized omnidirectional robotic platform integrated with ROS as a physical scale prototype.

In today's rapidly evolving industry, new challenges constantly arise that must be addressed quickly to keep up with the growing demand. Industry 5.0 aims to eliminate ob-stacles in production lines and integrate intelligent systems into manufacturing without excluding human operators from their daily tasks. In this context, developing software for controlling and managing data-driven evidence in the production cells becomes essential when considering performance, reliability, and practical aspects, as well as the financial returns it can bring to organizations. This work presents a brief overview of such an approach, proposing the development of a software architecture that manages industrial quality inspection in automotive parts using an Internet of Things publisher/subscriber model for controlling and synchronizing sub-processes. The results obtained demonstrate the benefits of implementing a control and evidence management system, offering production operators online monitoring of failures in manufactured parts and effective visualization of evidence of failures. This helps in decision-making and integrates the operator (user) with the system, which aligns with Industry 5.0 principles. This contribution is supported by creating a database of quality inspection evidence that can be used to train machine learning models to detect failures more accurately in the future.

This paper describes a digital-twin-based approach to dimensional control designed to assist quality inspection of automotive air-conditioning rotors in the vehicle manufacturing industry. It focuses on dimensional quality control within the field of dimensional metrology, aiming to accurately measure the dimensions of parts, subassemblies, and complete equipment systems. The proposed approach has created a prototype, collecting measurement data from a test sample to generate validation results for error profiling. The main goal is to develop a method for improving the precision of sensors used for this type of quality inspection.

In the fertilizer industry, granulometric analysis is an important quality control of the product. Usually, this process is done mechanically, through sieving, which makes the process slow. As an alternative to this, a methodology based on digital image processing for fertilizer classification is proposed. This approach allows to measure feret's diameter, area, volume and mass, important indicators to characterize fertilizers. The results obtained were validated with sieved samples and demonstrate the applicability of the method as a low cost alternative to the traditional method of granulometry of fertilizers.