For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

This project aims to develop an interactive control system for an autonomous service robot using an ROS (robot operating system). The system integrates an intuitive web interface and an interactive chatbot supported by Google Gemini to enhance the control experience and personalization for the user. The methodology includes the integration of an API (application programming interface) to access a database storing user preferences, such as speed and frequent destinations. Furthermore, the system employs facial recognition, people groups’ recognition, and adaptive responses from the chatbot for autonomous navigation, ensuring a service tailored to the individual needs of each user. To validate the proposal, it was implemented on an autonomous service robot, integrated into a motorized wheelchair. Tests demonstrated that the system effectively adjusts the wheelchair’s behavior to user preferences, resulting in safer and more personalized navigation. The use of facial recognition and chatbot interaction provided more intuitive and efficient control. The developed system significantly improves the autonomy and quality of life for wheelchair users, proving to be a viable and efficient solution for autonomous and personalized control. The results indicate that integrating technologies like ROS, intuitive web interfaces, and interactive chatbots can transform the user experience of autonomous wheelchairs, better meeting the specific needs of users.

A Programação Linear tem sido muito utilizada por diversos setores da indústria como forma de otimizar seus processos de produção, visando reduzir custos ou aumentar lucro. Embora já muito utilizada por alguns setores da aviação, da siderurgia e de móveis, alguns outros estão distantes, desta ferramenta por falta de conhecimento ou oportunidade. Por este motivo, neste trabalho propomos aplicá-la na linha de produção de uma cerâmica que fornece blocos e lajotas para um setor muito importante para o desenvolvimento econômico, a Construção Civil. Os resultados e conclusões obtidos serão apresentados ao final deste artigo.