Browsing by Author "Gonçalves, Gil"
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- Data analysis for trajectory generation for a robot manipulator using data from a 2D industrial laserPublication . Gomes, Diogo; Alvarez, Mariano José; Brancalião, Laiany; Carneiro, Jorge; Gonçalves, Gil; Costa, Paulo Gomes da; Gonçalves, José; Pinto, Vítor H.Nowadays, the automation of factory floors is necessary for extensive manufacturing processes to meet the ever-increasing competitiveness of current markets. The technological advances applied to the digital platforms have led many businesses to automate their manufacturing processes, introducing robotic manipulators collaborating with human operators to achieve new productivity, manufacturing quality, and safety levels. However, regardless of the amount of optimization implemented, some quality problems may be introduced in production lines with many products being designed and produced. This project proposes a solution for feature extraction that can be applied to automatic shape- and position-detection using a 2-dimension (2D) industrial laser to extract 3-dimension (3D) data where the movement of the item adds the third dimension through the laser’s beam. The main goal is data acquisition and analysis. This analysis will later lead to the generation of trajectories for a robotic manipulator. The results of this application proved reliable given their small measurement error values of a maximum of 2 mm.
- Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and OpportunitiesPublication . Catti, Paolo; Freitas, Artur; Pereira, Eliseu; Gonçalves, Gil; Lopes, Rui Pedro; Nikolakis, Nikolaos; Alexopoulos, KosmasData analytics and Artificial Intelligence (AI) have emerged as essen- tial tools in manufacturing over recent years, providing better insight into pro- duction systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even pre- vent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are ac- cessible, the implementation of accurate, robust, standard, and economically vi- able quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today’s manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation chal- lenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are dis- cussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is pre- sented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.
- Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving ObstaclesPublication . Cadete, Tomás; Pinto, Vítor H.; Lima, José; Gonçalves, Gil; Costa, Paulo Gomes daAutonomous Mobile Robots (AMRs) have significantly transformed task management in factories, warehouses, and urban environments. These robots enhance operational efficiency, reduce labor costs, and automate various tasks. However, navigating dynamic environments with moving obstacles, such as human workers, vehicles, and machinery, remains challenging. Traditional navigation systems, which rely on static maps and predefined routes, struggle to adapt to these dynamic settings. This research addresses these limitations by developing a dynamic navigation system that improves AMR performance in industrial and urban scenarios. The system enhances the A* algorithm to account for the current positions and predicted trajectories of moving obstacles, allowing the AMR to navigate safely and efficiently. Advanced sensor technologies, such as LiDAR and stereo cameras, are utilized for real-time environmental perception. The system integrates trajectory prediction and an Artificial Potential Field (APF) method for emergency collision avoidance. The solution is implemented using the Gazebo simulator and the Robot Operating System (ROS2), ensuring real-time operation and adaptive path planning. This research aims to significantly improve AMR safety, efficiency, and adaptability in dynamic environments.