Abstract
With the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

Abstract
The growing demand for high-quality components in various industries, particularly in the automotive sector, requires advanced and reliable inspection methods to maintain competitive standards and support innovation. Manual quality inspection tasks are often inefficient and prone to errors due to their repetitive nature and subjectivity, which can lead to attention lapses and operator fatigue. The inspection of reflective aluminum parts presents additional challenges, as uncontrolled reflections and glare can obscure defects and reduce the reliability of conventional vision-based methods. Addressing these challenges requires optimized illumination strategies and robust image processing techniques to enhance defect visibility. This work presents the development of an automated optical inspection system for reflective parts, focusing on components made of high-pressure diecast aluminum used in the automotive industry. The reflective nature of these parts introduces challenges for defect detection, requiring optimized illumination and imaging methods. The system applies deep learning algorithms and uses dome light to achieve uniform illumination, enabling the detection of small defects on reflective surfaces. A collaborative robotic manipulator equipped with a gripper handles the parts during inspection, ensuring precise positioning and repeatability, which improves both the efficiency and effectiveness of the inspection process. A flow execution-based software platform integrates all system components, enabling seamless operation. The system was evaluated with Schmidt Light Metal Group using three custom datasets to detect surface porosities and inner wall defects post-machining. For surface porosity detection, YOLOv8-Mosaic, trained with cropped images to reduce background noise, achieved a recall value of 84.71% and was selected for implementation. Additionally, an endoscopic camera was used in a preliminary study to detect defects within the inner walls of holes. The industrial trials produced promising results, demonstrating the feasibility of implementing a vision-based automated inspection system in various industries. The system improves inspection accuracy and efficiency while reducing material waste and operator fatigue.

Abstract
The 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 center dot flow, of a symbiotic aquaponic system to farm algae and fish. The smart GREEN center dot 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.

Abstract
Quality inspection inspection systems are critical for maintaining product integrity. Being a repetitive task, when performed by operators only, it can be slow and error-prone. This paper introduces an automated inspection system for quality assessment in casting aluminum parts resorting to a robotic system. The method comprises two processes: filing detection and hole inspection. For filing detection, five deep learning modes were trained. These models include an object detector and four instance segmentation models: YOLOv8, YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, and Mask R-CNN, respectively. Among these, YOLOv8s-seg exhibited the best overall performance, achieving a recall rate of 98.10%, critical for minimizing false negatives and yielding the best overall results. Alongside, the system inspects holes, utilizing image processing techniques like template-matching and blob detection, achieving a 97.30% accuracy and a 2.67% Percentage of Wrong Classifications. The system improves inspection precision and efficiency while supporting sustainability and ergonomic standards, reducing material waste and reducing operator fatigue. © The Author(s) 2025.

Abstract
Agriculture needs to produce more with fewer resources to satisfy the world's demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier. This research addresses the challenges posed by low-cost manipulators, such as inaccuracy, limited sensor feedback, and dynamic uncertainties. Three control strategies for a low-cost agricultural SCARA manipulator were developed and benchmarked: a Sliding Mode Controller (SMC), a Reinforcement Learning (RL) Controller, and a novel Proportional-Integral (PI) controller with a self-tuning feedforward element (PIFF). The results show the best response time was obtained using the SMC, but with joint movement jitter. The RL controller showed sudden breaks and overshot upon reaching the setpoint. Finally, the PIFF controller showed the smoothest reference tracking but was more susceptible to changes in system dynamics.