Human-robot collaboration for unknown flexible surface exploration and treatment based on mesh iterative learning control
Human-robot collaboration for unknown flexible surface exploration and treatment based on mesh iterative learning control
Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.
7923-7930
Xia, Jingkang
8e4d1903-377f-4d8c-b6d1-910d7f965091
Widanage, Kithmi Nima Dickwella
a2734ee5-21d5-4dbb-839b-27b3865304b3
Zhang, Ruiqing
23924eda-187a-45df-8e47-c12ed5593334
Parween, Rizuwana
cf11cabd-333d-404d-b917-9fdcc007da5d
Godaba, Hareesh
787c1482-6a29-43ad-b49e-a6a2b7175f0c
Herzig, Nicolas
dfdfb04d-e59f-43b9-b35e-795d53dfbee6
Glovnea, Romeo
8b0e656f-4ec3-4bac-ac8f-642a9fa8f88e
Huang, Deqing
96e466d6-59e1-4428-a6f6-4c1cecd45d00
Li, Yanan
c63b9e6a-cb8f-448b-a128-541ecea09782
13 December 2023
Xia, Jingkang
8e4d1903-377f-4d8c-b6d1-910d7f965091
Widanage, Kithmi Nima Dickwella
a2734ee5-21d5-4dbb-839b-27b3865304b3
Zhang, Ruiqing
23924eda-187a-45df-8e47-c12ed5593334
Parween, Rizuwana
cf11cabd-333d-404d-b917-9fdcc007da5d
Godaba, Hareesh
787c1482-6a29-43ad-b49e-a6a2b7175f0c
Herzig, Nicolas
dfdfb04d-e59f-43b9-b35e-795d53dfbee6
Glovnea, Romeo
8b0e656f-4ec3-4bac-ac8f-642a9fa8f88e
Huang, Deqing
96e466d6-59e1-4428-a6f6-4c1cecd45d00
Li, Yanan
c63b9e6a-cb8f-448b-a128-541ecea09782
Xia, Jingkang, Widanage, Kithmi Nima Dickwella, Zhang, Ruiqing, Parween, Rizuwana, Godaba, Hareesh, Herzig, Nicolas, Glovnea, Romeo, Huang, Deqing and Li, Yanan
(2023)
Human-robot collaboration for unknown flexible surface exploration and treatment based on mesh iterative learning control.
In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
IEEE.
.
(doi:10.1109/IROS55552.2023.10341612).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.
This record has no associated files available for download.
More information
Published date: 13 December 2023
Identifiers
Local EPrints ID: 499672
URI: http://eprints.soton.ac.uk/id/eprint/499672
PURE UUID: ad1ced86-dbd0-4719-99e3-1542a8376b0c
Catalogue record
Date deposited: 31 Mar 2025 16:40
Last modified: 01 Apr 2025 02:13
Export record
Altmetrics
Contributors
Author:
Jingkang Xia
Author:
Kithmi Nima Dickwella Widanage
Author:
Ruiqing Zhang
Author:
Rizuwana Parween
Author:
Hareesh Godaba
Author:
Nicolas Herzig
Author:
Romeo Glovnea
Author:
Deqing Huang
Author:
Yanan Li
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics