Abstract
Hexapod robots, with their six-legged design, excel in stability and adaptability on challenging terrain but pose significant control challenges due to their high degrees of freedom. While reinforcement learning (RL) has been explored for robot navigation, few studies have systematically compared on-policy and off-policy methods for multi-legged locomotion. This work presents a comparative study of SARSA and Q-Learning for trajectory control of a simulated hexapod robot, focusing on the influence of learning rate (alpha), discount factor (gamma), and eligibility trace (lambda). The evaluation spans eight initial poses, with performance measured through lateral deviation (Ey), orientation error (E theta), and iteration count. Results show that Q-Learning generally achieves faster convergence and greater stability, particularly with higher gamma and lambda values, while SARSA can achieve competitive accuracy with careful parameter tuning. The findings demonstrate that eligibility traces substantially improve learning precision and provide practical guidelines for robust RL-based control in multi-legged robotic systems.

Abstract

Abstract

Abstract
The 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. © 2025 Elsevier B.V., All rights reserved.

Abstract
The increasing demand for automated quality inspection in modern industry, particularly for transparent and reflective parts, has driven significant interest in vision-based technologies. These components pose unique challenges due to their optical properties, which often hinder conventional inspection techniques. This systematic review analyzes 24 peer-reviewed studies published between 2015 and 2025, aiming to assess the current state of the art in computer vision-based inspection systems tailored to such materials. The review synthesizes recent advancements in imaging setups, illumination strategies, and deep learning-based defect detection methods. It also identifies key limitations in current approaches, particularly regarding robustness under variable industrial conditions and the lack of standardized benchmarks. By highlighting technological trends and research gaps, this work offers valuable insights and directions for future research-emphasizing the need for adaptive, scalable, and industry-ready solutions to enhance the reliability and effectiveness of inspection systems for transparent and reflective parts.

Abstract
In an era where robots are becoming an integral part of human quotidian activities, understanding how they function is crucial. Among the inherent building complexities, from electronics to mechanics, path planning emerges as a universal aspect of robotics. The primary contribution of this work is to provide an overview of the current state of robot path planning topics and a comparison between those same algorithms and its inherent characteristics. The path planning concept relies on the process by which an algorithm determines a collision-free path between a start and an end point, optimizing parameters such as energy consumption and distance. The quest for the most effective path planning method has been a long-standing discussion, as the choice of method is highly dependent on the specific application. This review consolidates and elucidates the categories of path planning methods, specifically classical or analytical methods, and computer intelligence methods. In addition, the operational principles of these categories will be explored, discussing their respective advantages and disadvantages, and reinforcing these discussions with relevant studies in the field. This work will focus on the most prevalent and recognized methods within the robotics path planning problem, being mobile robotics or manipulator arms, including Cell Decomposition, A*, Probabilistic Roadmaps, Rapidly-exploring Random Trees, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Potential Fields, Fuzzy, and Neural Networks. Following the detailed explanation of these methods, a comparative analysis of their advantages and drawbacks is organized in a comprehensive table. This comparison will be based on various quality metrics, such as the type of trajectory provided (global or local), the scenario implementation type (real or simulated scenarios), testing environments (static or dynamic), hybrid implementation possibilities, real-time implementation, completeness of the method, consideration of the robot's kinodynamic constraints, use of smoothing techniques, and whether the implementation is online or offline.