Preface of the thirdinternationalworkshop on Empowering People in Dealing with Internet of Things Ecosystems. The EMPATHY project has been funded by the Italian Ministry of Education, Universities and Research (MIUR...
详细信息
Large RNA molecules often carry multiple functional domains whose spatial arrangement is an important determinant of their function. Pre-mRNA splicing, furthermore, relies on the spatial proximity of the splice juncti...
详细信息
Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches ...
详细信息
ISBN:
(数字)9798350375022
ISBN:
(纸本)9798350375039
Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches have emerged in intuitive robot programming. These inventive systems, rooted in diverse conceptual frameworks, are designed to expedite the deployment of robot systems. A prominent innovation in this domain is adopting no-code robot programming through finger-based gestures. A robot program can be generated by capturing and tracking non-expert users’ finger movements and gestures, converting 3D coordinates into an executable robot programming language. However, accurately determining finger positions for 3D coordinates and precise geometrical features presents an ongoing challenge. In pursuit of heightened trajectory precision and reducing more significant effort for the users, we propose a hybrid methodology that amalgamates finger-gesture programming with point cloud data. This synergistic integration demonstrates promising outcomes, substantiating its potential to facilitate the precise and adaptive generation of robot paths within robot applications.
Capturing real-world 3D spaces as point clouds is efficient and descriptive, but it comes with sensor errors and lacks object parametrization. These limitations render point clouds unsuitable for various real-world ap...
详细信息
ISBN:
(数字)9798350375022
ISBN:
(纸本)9798350375039
Capturing real-world 3D spaces as point clouds is efficient and descriptive, but it comes with sensor errors and lacks object parametrization. These limitations render point clouds unsuitable for various real-world applications, such as robot programming, without extensive post-processing (e.g., outlier removal, semantic segmentation). On the other hand, CAD modeling provides high-quality, parametric representations of 3D space with embedded semantic data, but requires manual component creation that is time-consuming and costly. To address these challenges, we propose a novel solution that combines the strengths of both approaches. Our method for 3D workcell sketching from point clouds allows users to refine raw point clouds using an Augmented Reality (AR) interface that leverages their knowledge and the real-world 3D environment. By utilizing a toolbox and an AR-enabled pointing device, users can enhance point cloud accuracy based on the device’s position in 3D space. We validate our approach by comparing it with ground truth models, demonstrating that it achieves a mean error within 1cm — significant improvement over standard LiDAR scanner apps.
暂无评论