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- 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.
- Traction characterization in the robocup middle size leaguePublication . Dias, André; Soares Almeida, José Miguel; Martins, Alfredo; Silva, EduardoIn this work the problem of traction af mobile wheeled robots for the particular case of Robocup MSL league was.analyzed. In particular the slip occurrence in differential drive DC electrical powered mobile robots was studied. Traction loss was characterized for the set of possible game events ranging from excessive acceleration, centripetal force effects, to collisions (either to fixed obstacles ar by external players). The traction analysis was performed with measurements of electrical current on each motor and odometry and inertial data. An approach to overall traction control relying in electrical current data coupled with motion data, is envisioned. This approach does not depend on apriori knowledge of the operating surface or robot motor model.
- 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.
- Small fixed wing autonomous aerial vehicle for forest management applicationsPublication . Martins, Alfredo; Almeida, José; Silva, Eduardo; Dias Silva, Hugo Filipe; Bento, Domingos; Figueiredo, André; Santos, FilipeIn this work a forest management infrastructure solution using small autonomous aerial vehicles is proposed. The FALCOS unmanned aerial vehicle developed for remotemonitoring purposes is described. This is a small size UAV with onboard vision processing and autonomous flight capabilities. A set of custom developed navigation sensors was developed for the vehicle. Fire detection is performed through the use of low cost digital cameras and near-infrared sensors. This approach is extended to a radiometric forest inventory and forest fire danger characterization. Test results for navigation and ignition detection in real scenario are presented.
- 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.
- 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.
- Unraveling emotions with pre-trained modelsPublication . Pajón-Sanmartín, Alejandro; Arriba-Pérez, Francisco de; García-Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo-Rial, Juan Carlos; BENEDITA CAMPOS NEVES MALHEIRO, MARIATransformer models have significantly advanced the field of emotion recognition. However, there are still open challenges when exploring open-ended queries for Large Language Models (LLMs). Although current models offer good results, automatic emotion analysis in open texts presents significant challenges, such as contextual ambiguity, linguistic variability, and difficulty interpreting complex emotional expressions. These limitations make the direct application of generalist models difficult. Accordingly, this work compares the effectiveness of fine-tuning and prompt engineering in emotion detection in three distinct scenarios: (i) performance of fine-tuned pre-trained models and general-purpose LLMs using simple prompts; (ii) effectiveness of different emotion prompt designs with LLMs; and (iii) impact of emotion grouping techniques on these models. Experimental tests attain metrics above 70 % with a fine-tuned pre-trained model for emotion recognition. Moreover, the findings highlight that LLMs require structured prompt engineering and emotion grouping to enhance their performance. These advancements improve sentiment analysis, human-computer interaction, and understanding of user behavior across various domains.
- 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.
- Online detection and infographic explanation of spam reviews with data drift adaptationPublication . de Arriba Pérez, Francisco; García Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo, Juan C.Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure.
- Exposing and explaining fake news on-the-flyPublication . de Arriba Pérez, Francisco; García Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo, Juan C.Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.