6 research outputs found
RGB-D Tracking and Optimal Perception of Deformable Objects
Addressing the perception problem of texture-less objects that undergo large deformations and movements, this article presents a novel RGB-D learning-free deformable object tracker in combination with a camera position optimisation system for optimal deformable object perception. The approach is based on the discretisation of the object''s visible area through the generation of a supervoxel graph that allows weighting new supervoxel candidates between object states over time. Once a deformation state of the object is determined, supervoxels of its associated graph serve as input for the camera position optimisation problem. Satisfactory results have been obtained in real time with a variety of objects that present different deformation characteristics
Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls
In this paper, we present a new vision-based method to control the shape of
elastic rods with robot manipulators. Our new method computes parameterized
regression features from online sensor measurements that enable to
automatically quantify the object's configuration and establish an explicit
shape servo-loop. To automatically deform the rod into a desired shape, our
adaptive controller iteratively estimates the differential transformation
between the robot's motion and the relative shape changes; This valuable
capability allows to effectively manipulate objects with unknown mechanical
models. An auto-tuning algorithm is introduced to adjust the robot's shaping
motion in real-time based on optimal performance criteria. To validate the
proposed theory, we present a detailed numerical and experimental study with
vision-guided robotic manipulators.Comment: 13 pages, 22 figures, 2 table
Vision-based Manipulation of Deformable and Rigid Objects Using Subspace Projections of 2D Contours
This paper proposes a unified vision-based manipulation framework using image
contours of deformable/rigid objects. Instead of using human-defined cues, the
robot automatically learns the features from processed vision data. Our method
simultaneously generates---from the same data---both, visual features and the
interaction matrix that relates them to the robot control inputs. Extraction of
the feature vector and control commands is done online and adaptively, with
little data for initialization. The method allows the robot to manipulate an
object without knowing whether it is rigid or deformable. To validate our
approach, we conduct numerical simulations and experiments with both deformable
and rigid objects