000K  utf8
0100  1839392983
1100  2023$c2023-03-16
1500  eng
2050  urn:nbn:de:gbv:27-dbt-20230330-074834-002
2051  10.3389/frobt.2023.1120357
3000  Zhang, Yan
3010  Fütterer, Richard
3010  Notni, Gunther
4000  Interactive robot teaching based on finger trajectory using multimodal RGB-D-T-data  [Zhang, Yan]
4060  13 Seiten
4209  The concept of Industry 4.0 brings the change of industry manufacturing patterns that become more efficient and more flexible. In response to this tendency, an efficient robot teaching approach without complex programming has become a popular research direction. Therefore, we propose an interactive finger-touch based robot teaching schema using a multimodal 3D image (color (RGB), thermal (T) and point cloud (3D)) processing. Here, the resulting heat trace touching the object surface will be analyzed on multimodal data, in order to precisely identify the true hand/object contact points. These identified contact points are used to calculate the robot path directly. To optimize the identification of the contact points we propose a calculation scheme using a number of anchor points which are first predicted by hand/object point cloud segmentation. Subsequently a probability density function is defined to calculate the prior probability distribution of true finger trace. The temperature in the neighborhood of each anchor point is then dynamically analyzed to calculate the likelihood. Experiments show that the trajectories estimated by our multimodal method have significantly better accuracy and smoothness than only by analyzing point cloud and static temperature distribution.
4950  https://doi.org/10.3389/frobt.2023.1120357$xR$3Volltext$534
4950  https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20230330-074834-002$xR$3Volltext$534
4961  http://uri.gbv.de/document/gvk:ppn:1839392983
5051  004
5550  finger trajectory recognition
5550  meshless finite difference solution
5550  multimodal image processing
5550  point cloud processing
5550  RGB-D-T-data
5550  robot teaching