Curve Fitting-Based Deformation Tracking for Vision-Based Robotic Applications

摘要

Application of robotics on production lines often involves handling flexible objects (such as items of natural origin or plastic bags containing liquid/bulk substances), which makes it crucial to consider the shape of an item before and after it has been affected by robotic manipulation. Most of the time deformable items are challenging for the robot in such operations as grasping, cutting, or packaging. The objective of this paper is to track object deformations and perform a task based on this information. The paper addresses issues in tracking object deformation and proposes a solution for deformation tracking to form preliminary knowledge and scene awareness on the robot side. A curve-fitting-based method was implemented to define a region of interest using images from a RealSense D415 camera. The developed approach identifies the maximum number of aligned points and uses it to determine where the deformation occurred. The results of this research show that the deformations are efficiently tracked. Utilising the algorithm proposed in this paper, an efficient method capable of making the robot aware of the deformation present in the scene is demonstrated. This approach is applicable in domains such as food processing, healthcare, and other fields where gentle and precise manipulations are required. The method is useful in industrial applications in which deformation cannot be completely avoided but still needs to be tracked.