3 research outputs found
Enabling Grasp Action: Generalized Evaluation of Grasp Stability via Contact Stiffness from Contact Mechanics Insight
Performing a grasp is a pivotal capability for a robotic gripper. We propose
a new evaluation approach of grasping stability via constructing a model of
grasping stiffness based on the theory of contact mechanics. First, the
mathematical models are built to explore soft contact and the general grasp
stiffness between a finger and an object. Next, the grasping stiffness matrix
is constructed to reflect the normal, tangential and torsion stiffness
coefficients. Finally, we design two grasping cases to verify the proposed
measurement criterion of grasping stability by comparing different grasping
configurations. Specifically, a standard grasping index is used and compared
with the minimum eigenvalue index of the constructed grasping stiffness we
built. The comparison result reveals a similar tendency between them for
measuring the grasping stability and thus, validates the proposed approach.Comment: 12 pages, 14 figure
Multi-Pen Robust Robotic 3D Drawing Using Closed-Loop Planning
This paper develops a flexible and robust robotic system for autonomous
drawing on 3D surfaces. The system takes 2D drawing strokes and a 3D target
surface (mesh or point clouds) as input. It maps the 2D strokes onto the 3D
surface and generates a robot motion to draw the mapped strokes using visual
recognition, grasp pose reasoning, and motion planning. The system is flexible
compared to conventional robotic drawing systems as we do not fix drawing tools
to the end of a robot arm. Instead, a robot selects drawing tools using a
vision system and holds drawing tools for painting using its hand. Meanwhile,
with the flexibility, the system has high robustness thanks to the following
crafts: First, a high-quality mapping method is developed to minimize
deformation in the strokes. Second, visual detection is used to re-estimate the
drawing tool's pose before executing each drawing motion. Third, force control
is employed to avoid noisy visual detection and calibration, and ensure a firm
touch between the pen tip and a target surface. Fourth, error detection and
recovery are implemented to deal with unexpected problems. The planning and
executions are performed in a closed-loop manner until the strokes are
successfully drawn. We evaluate the system and analyze the necessity of the
various crafts using different real-word tasks. The results show that the
proposed system is flexible and robust to generate a robot motion from picking
and placing the pens to successfully drawing 3D strokes on given surfaces
6DLS: Modeling Nonplanar Frictional Surface Contacts for Grasping using 6D Limit Surfaces
Robot grasping with deformable gripper jaws results in nonplanar surface
contacts if the jaws deform to the nonplanar local geometry of an object. The
frictional force and torque that can be transmitted through a nonplanar surface
contact are both three-dimensional, resulting in a six-dimensional frictional
wrench (6DFW). Applying traditional planar contact models to such contacts
leads to over-conservative results as the models do not consider the nonplanar
surface geometry and only compute a three-dimensional subset of the 6DFW. To
address this issue, we derive the 6DFW for nonplanar surfaces by combining
concepts of differential geometry and Coulomb friction. We also propose two 6D
limit surface (6DLS) models, generalized from well-known three-dimensional LS
(3DLS) models, which describe the friction-motion constraints for a contact. We
evaluate the 6DLS models by fitting them to the 6DFW samples obtained from six
parametric surfaces and 2,932 meshed contacts from finite element method
simulations of 24 rigid objects. We further present an algorithm to predict
multicontact grasp success by building a grasp wrench space with the 6DLS model
of each contact. To evaluate the algorithm, we collected 1,035 physical grasps
of ten 3D-printed objects with a KUKA robot and a deformable parallel-jaw
gripper. In our experiments, the algorithm achieves 66.8% precision, a metric
inversely related to false positive predictions, and 76.9% recall, a metric
inversely related to false negative predictions. The 6DLS models increase
recall by up to 26.1% over 3DLS models with similar precision