ISEP – DEE – Livro, parte de livro, ou capítulo de livro
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Percorrer ISEP – DEE – Livro, parte de livro, ou capítulo de livro por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- EPS@ISEP: Mapping sustainabilityPublication . Guedes, Pedro; BENEDITA CAMPOS NEVES MALHEIRO, MARIA; Arnó, Elisabete; Fuentes, Pedro; Malheiro, Benedita; Sánchez, Antonio J.; Monjo, LluisThe European Project Semester (EPS) is a project-based learning framework designed to prepare engineering undergraduates for future professional challenges. During one semester, multicultural multidisciplinary teams design, develop and test solutions to real problems affecting the planet, society or individuals, considering technical, sustainability, ethical, and market requirements. The implemented learning process gives teams the autonomy to make design decisions, as long as they are based on clear sustainability and ethical criteria. The aim is for students to develop skills not only in critical thinking, problem-solving, creativity, innovation, effective communication, collaboration, and teamwork, but above all, the ability to make decisions based on ethical and sustainability principles. This study aims to demonstrate that EPS@ISEP effectively contributes to the development of sustainability-oriented engineers. To this end, it compiles evidence found at the institutional level, represented by programme documentation, and the student level, represented by team deliverables. The method involves semi-automatic mapping of sustainability keywords compiled from the documents, as well as by other researchers. The results show that both the institution and the participants are aligned and committed to the design of sustainability-driven solutions.
- Mapping ethics in EPS@ISEP robotics projectsPublication . BENEDITA CAMPOS NEVES MALHEIRO, MARIA; Silva, Manuel; Ferreira, Paulo; Guedes, Pedro; Silva, Manuel F.; Tokhi, Mohammad Osman; A. Ferreira, Maria Isabel; Malheiro, Benedita; Guedes, Pedro; Ferreira, Paulo; Costa, Maria TeresaThe European Project Semester (EPS), offered by the Instituto Superior de Engenharia do Porto (ISEP), is a capstone programme designed for undergraduate students in engineering, product design, and business. EPS@ISEP fosters project-based learning, promotes multicultural and interdisciplinary teamwork, and ethics- and sustainability-driven design. This study applies Natural Language Processing techniques, specifically text mining, to analyse project papers produced by EPS@ISEP teams. The proposed method aims to identify evidence of ethical concerns within EPS@ISEP projects. An innovative keyword mapping approach is introduced that first defines and refines a list of ethics-related keywords through prompt engineering. This enriched list of keywords is then used to systematically map the content of project papers. The findings indicate that the EPS@ISEP robotics project papers analysed demonstrate awareness of ethical considerations and actively incorporate them into design processes. The method presented is adaptable to various application areas, such as monitoring compliance with responsible innovation or sustainability policies.
- VIRIATO: Visual-Action Reinforcement Integrator for Actor-Critic with Temporal ObservationsPublication . Campanhã, João; Neves, Francisco; Pinto, Andry; BENEDITA CAMPOS NEVES MALHEIRO, MARIA; Fonseca, Pedro; Moreira, António; Neto, Pedro; Morais, Pedro; Lima, JoséThe Visual-Action Reinforcement Integrator for Actor-Critic with Temporal Observations (VIRIATO) is a compact multi-input feature-extractor architecture designed to enable robust visual navigation of Unmanned Aerial Vehicles (UAVs) conducting close-range inspection of photovoltaic arrays. The target task of low-altitude flight over dynamic, visually variable surfaces without privileged information is inherently partially observable. VIRIATO augments stacked image observations with a short history of recent past actions, producing a richer latent state for the Soft Actor-Critic (SAC) agent. Training is performed with domain randomization to expose the policy to diverse lighting, backgrounds and panel layouts. In simulation, VIRIATO yields faster learning and improved sample efficiency compared to a standard image-only Convolutional Neural Network (CNN) feature extractor, achieving lower position and yaw errors and substantially better robustness under image perturbations while retaining high task completion rates. The architecture is intentionally simple and general: it improves temporal awareness without adding complex recurrence, and it could be adapted to other perception-driven robotic tasks. These results demonstrate that integrating historical action data with visual encoding, together with domain randomization, is an effective way to achieve reliable autonomous vision-based navigation.