2 research outputs found
Iterative Surface Mapping Using Local Geometry Approximation with Sparse Measurements During Robotic Tooling Tasks
We present a cost-efficient and versatile method to map an unknown 3D
freeform surface using only sparse measurements while the end-effector of a
robotic manipulator moves along the surface. The geometry is locally
approximated by a plane, which is defined by measured points on the surface.
The method relies on linear Kalman filters, estimating the height of each point
on a 2D grid. Therefore, the approximation covariance for each grid point is
determined using a radial basis function to consider the measured point
positions. We propose different update strategies for the grid points
exploiting the locality of the planar approximation in combination with a
projection method. The approach is experimentally validated by tracking the
surface with a robotic manipulator. Three laser distance sensors mounted on the
end-effector continuously measure points on the surface during the motion to
determine the approximation plane. It is shown that the surface geometry can be
mapped reasonably accurate with a mean absolute error below 1 mm. The mapping
error mainly depends on the size of the approximation area and the curvature of
the surface.Comment: Accepted for presentation at and publication in the proceedings of
the 2021 IEEE 17th International Conference on Automation Science and
Engineering (CASE), 7 pages, 6 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