ISEP - LSA - Laboratório de Sistemas Autónomos
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LSA is a R&D unit from ISEP the Engineering School of Porto Polytechnic. It conducts research in autonomous systems and related areas such as navigation, control and coordination of multiple robots.The laboratory activity is developed in four lines of work:
R&D programs;
Educational project;
Dissemination projects;
Strategic positioning projects for the school ISEP/IPP.
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Percorrer ISEP - LSA - Laboratório de Sistemas Autónomos por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- Algae and Fish Farming: An EPS@ISEP 2022 ProjectPublication . Blomme, Rose-Farah; Domissy, Zoé; Dylik, Zuzanna; Hidding, Thomas; Röhe, Alina; Duarte, Abel J.; BENEDITA CAMPOS NEVES MALHEIRO, MARIA; JUSTO, Jorge; Ferreira, Paulo; Guedes, Pedro; Castro Ribeiro, Maria Cristina de; Silva, Manuel; Auer, Michael E.; Rüütmann, TiiaThe European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design program where students, organised in multidisciplinary and multicultural teams, create a solution for a proposed problem, bearing in mind ethical, sustainability and market concerns. The project proposals are usually aligned with the United Nations Sustainable Development Goals (SDG). New sustainable food production methods are essential to cope with the continuous population growth and aligned with SDG2 and SDG12. In this context, this paper describes the research and work done by a team of Erasmus students enrolled in EPS@ISEP during the spring of 2022. Since sustainable algae farming can be a suitable source of food, the team's goal was the design and develop a proof-of-concept prototype, named GREEN·flow, of a symbiotic aquaponic system to farm algae and fish. The smart GREEN·flow concept comprises a modular structure and an app for control and supervision. The proposed design was driven by state-of-the-art research, targeted to a specific market niche based on a market analysis, and considering sustainability and ethics concerns, all of which are described in this manuscript. A proof-of-concept prototype was built and tested to verify that it worked as intended.
- Breeding endangered beetles - An EPS@ISEP 2024 ProjectPublication . Florus, Cedric; Lattunen, Jennifer; Knäuper, Julius; Jugiel, Krzysztof; Silva, Marion; Dekkers, Thomas; Duarte, Abel J.; BENEDITA CAMPOS NEVES MALHEIRO, MARIA; Santos Ribeiro, Maria Cristina; JUSTO, Jorge; Ferreira, Paulo; Silva, Manuel; Guedes, Pedro; Auer, Michael E.; Rüütmann, TiiaHabitat loss, climate change, and pesticide use are key threats affecting beetle populations. This paper describes Scarabreed, a project that contributes to mitigate the beetle decline crisis. It was carried out by a team of six European students from different engineering fields and nationalities within the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP), a project-based and teamwork learning framework. The designed solution -- the Beetle Breeder Version 2 (BBV2) -- consists of a smart modular vivarium created especially for beetle breeding. It monitors and controls relevant habitat parameters and offers two user-friendly interfaces (on-device and a Web application). The innovative modular structure of the vivarium allows easy scaling, customisation, and transportation. As a whole, the project offers significant environmental benefits: (i) facilitates the captive breeding of endangered beetle species, promoting population restoration efforts; (ii) fosters, as an educational tool, youth and general public awareness about the crucial role beetles play in ecosystems; and (iii) adopts eco-efficient and responsible business practices by following ethics and sustainability driven design and marketing.
- Engineering a Sustainable Future with EPS@ISEPPublication . Malheiro, Benedita; Guedes, Pedro; Leal Filho, Walter; Gasparetto Rebelatto, Bianca; Annelin, Alice; Boström, Gert-OlofThe challenge of engineering education is to transform engineering students into agents of innovation and well-being. In addition to solid scientific and technical knowledge, critical thinking, problem-solving and interpersonal competencies, it implies the ability to design and implement solutions supported by ethical and sustainability principles. With this goal in mind, the European Project Semester (EPS) provides a student-centred project-based learning framework. It is offered by a group of European higher education institutions, including the Instituto Superior de Engenharia do Porto (ISEP), the engineering school of the Polytechnic of Porto. Students work in teams of four to six, from different fields of study and nationalities, to design solutions to problems that affect individuals, society or the planet, taking into account the state of the art, the market and the ethical and sustainability implications of their decisions. These solutions are then implemented in a proof-of-concept prototype. Most of the projects address problems in education, the environment, food production and smart cities and have a strong educational, ethical and sustainability drive, encouraging students to develop sustainability competencies. This work analyses team papers of illustrative EPS@ISEP projects searching for evidences of the development of sustainability competencies. The proposed method maps keywords related to the sixteen United Nations Sustainable Development Goals to the contents of team papers by applying natural language processing and reusing the list of SDG keywords proposed by Auckland University. The results confirm EPS@ISEP fosters sustainability competencies in engineering undergraduates.
- 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.
- An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of PortugalPublication . García-Méndez, Silvia; Arriba-Pérez, Francisco de; Leal, Fátima; Veloso, Bruno; Malheiro, Benedita; Burguillo-Rial, Juan Carlos; BENEDITA CAMPOS NEVES MALHEIRO, MARIAThe public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the MetroPT data set from the metro operator of Porto, Portugal. The results are above 98 % for F-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high F-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.
- Identification and explanation of disinformation in wiki data streamsPublication . Arriba-Pérez, Francisco de; García-Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo, Juan C.Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers’ critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model’s prediction. Experimental results on two datasets attain approximately 90% values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.
- 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.
- Real-time vision system for mobile roboticsPublication . Silva, H. M.; Martins, A.; Soares Almeida, José Miguel; Oliveira Lima, Luís Carlos; Silva, E. P.; Patacho, A.This paper describes a real-time vision architecture for mobile robotics. It is integrated in the research program on mobile robotics pursued at the Autonomous Systems Lab ISEP-IPP. The implemented architecture is characterized by: low computational cost, low latency, low power, highly modularity, configurability, adaptability and scalability. A new method using run length encoding (RLE) colour transition allows real-time edge determination at low computational cost. A pipeline structure further reduces latency and allows a paralleled hardware implementation. A dedicated hardware vision sensor was developed in order to take advantage of the proposed architecture. The real-time characteristics and hardware partial implementation, coupled with low energy consumption address typical of autonomous systems applications.
- Roaz autonomous surface vehicle design and implementationPublication . Martins, A.; Ferreira, Hugo; Dias, A.; Almeida, C.; Silva, E. P.; Soares Almeida, José MiguelThe design of an Autonomous Surface Vehicle for operation in fiver and estuarine scenarios is presented. Multiple operations with autonomous underwater vehicles and support to AUV missions are one of the main design goals in the ROAZ system. The mechanical design issues are discussed. Hardware, software and implementation status are described along with the control and navigation system architecture. Some preliminary test results concerning a custom developed thruster are presented along with hydrodynamic drag calculations by the use of computer fluid dynamic methods.
- ROS-enabled DIY and open-source wheeled robots for higher education learning and competitions: A systematic reviewPublication . Pereira, Rúben; BENEDITA CAMPOS NEVES MALHEIRO, MARIA; Silva, ManuelThis study systematically characterizes Do It Yourself (DIY) and open-source wheeled robotic platforms used in higher education and academic competitions. It also analyzes Robot Operating System (ROS)-based designs with respect to real-time performance and multi-sensor integration, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A total of 20 high-quality studies were identified across five major digital libraries (Dimensions, Web of Science, SpringerLink, ScienceDirect, and IEEE Xplore), which were searched on 12 January 2026. Eligibility was restricted to peer-reviewed English-language studies published between 2005 and 2026 that explicitly implement ROS-based wheeled platforms in higher education contexts. Results were synthesized through qualitative analysis using a structured data extraction form implemented in the Parsifal systematic review platform. Methodological quality and risk of bias were assessed using a structured appraisal checklist. The results show a dominant trend toward distributed dual-processor architectures, which separate low-level real-time control from high-level processing. Most platforms target an accessible price range of 50€ to 500€ for open-source and DIY platforms. ROS has emerged as the standard middleware, enabling multi-sensor integration and supporting digital twin workflows. There is also a clear shift toward open-source hardware and Three-Dimensional (3D)-printed modular designs, which reduce production costs. However, challenges remain, including software obsolescence and the lack of maintenance plans. The findings highlight the need for interoperable reference architectures and automated deployment workflows to ensure long-term sustainability. Evidence is limited by heterogeneity, inconsistent reporting, and small sample sizes, which introduce risks of bias and imprecision. This review was formally registered with protocols.io.