Assisted painting of 3D structures using shared control with a hand-held robot
File(s)
Accepted version
Author(s)
Elsdon, J
Demiris, Y
Type
Conference Paper
Abstract
Abstract— We present a shared control method of painting
3D geometries, using a handheld robot which has a single
autonomously controlled degree of freedom. The user scans
the robot near to the desired painting location, the single
movement axis moves the spray head to achieve the required
paint distribution. A simultaneous simulation of the spraying
procedure is performed, giving an open loop approximation
of the current state of the painting. An online prediction of
the best path for the spray nozzle actuation is calculated in
a receding horizon fashion. This is calculated by producing a
map of the paint required in the 2D space defined by nozzle
position on the gantry and the time into the future. A directed
graph then extracts its edge weights from this paint density map
and Dijkstra’s algorithm is then used to find the candidate for
the most effective path. Due to the heavy parallelisation of this
approach and the majority of the calculations taking place on a
GPU we can run the prediction loop in 32.6ms for a prediction
horizon of 1 second, this approach is computationally efficient,
outperforming a greedy algorithm. The path chosen by the
proposed method on average chooses a path in the top 15%
of all paths as calculated by exhaustive testing. This approach
enables development of real time path planning for assisted
spray painting onto complicated 3D geometries. This method
could be applied to applications such as assistive painting for
people with disabilities, or accurate placement of liquid when
large scale positioning of the head is too expensive.
3D geometries, using a handheld robot which has a single
autonomously controlled degree of freedom. The user scans
the robot near to the desired painting location, the single
movement axis moves the spray head to achieve the required
paint distribution. A simultaneous simulation of the spraying
procedure is performed, giving an open loop approximation
of the current state of the painting. An online prediction of
the best path for the spray nozzle actuation is calculated in
a receding horizon fashion. This is calculated by producing a
map of the paint required in the 2D space defined by nozzle
position on the gantry and the time into the future. A directed
graph then extracts its edge weights from this paint density map
and Dijkstra’s algorithm is then used to find the candidate for
the most effective path. Due to the heavy parallelisation of this
approach and the majority of the calculations taking place on a
GPU we can run the prediction loop in 32.6ms for a prediction
horizon of 1 second, this approach is computationally efficient,
outperforming a greedy algorithm. The path chosen by the
proposed method on average chooses a path in the top 15%
of all paths as calculated by exhaustive testing. This approach
enables development of real time path planning for assisted
spray painting onto complicated 3D geometries. This method
could be applied to applications such as assistive painting for
people with disabilities, or accurate placement of liquid when
large scale positioning of the head is too expensive.
Date Issued
2017-07-24
Date Acceptance
2017-01-15
Citation
2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp.4891-4897
Publisher
IEEE
Start Page
4891
End Page
4897
Journal / Book Title
2017 IEEE International Conference on Robotics and Automation (ICRA)
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://ieeexplore.ieee.org/document/7989566/
Source
IEEE International Conference on Robotics and Automation
Publication Status
Published
Start Date
2017-05-29
Finish Date
2017-06-03
Coverage Spatial
Singapore
Date Publish Online
2017-07-24