Adaptive Visual Servoing Control of Closed-Architecture Robots With Both Visibility Constraints and Tracking Error Constraints

Although there are significant achievements in adaptive visual servoing control of robots with the consideration of visibility constraints or tracking error constraints, most of these approaches are limited to addressing one aspect of the two constraint issues. This article presents a novel adaptive visual servoing control solution specifically designed for robots with closed architecture, effectively managing both visibility constraints and tracking error constraints simultaneously. First, by appropriately combining the image-space position-based constraining function and tracking error-based constraining function, we develop a novel composite barrier function (CBF), enabling the transformation of the visual servoing system with both visibility constraints and tracking error constraints into a functionally equivalent “unconstrained” one. By stabilizing the “unconstrained” system, both visibility constraints and tracking error constraints are handled simultaneously for the first time. Then, we introduce a CBF-based adaptive visual servoing loop controller, designed to dynamically compute the position or velocity commands for the torque control loop. The controller's proven ability to guarantee the convergence of the visual servoing system, without modifying the manufacturer-embedded torque controller, renders it suitable for a wide range of industrial/commercial robots with closed architecture. Experiments are conducted on an industrial manipulator to exhibit the superior capabilities of the method.

Authors

Institutions

Publication Details

Journal
IEEE/ASME Transactions on Mechatronics
Published
2024-12-04
DOI
https://doi.org/10.1109/tmech.2024.3499324
Citations
4
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
1.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive Visual Servoing Control of Closed-Architecture Robots With Both Visibility Constraints and Tracking Error Constraints

Yafeng Li, Keli Pang, Choon Ki Ahn, Changchun Hua et al.
4 citations
IEEE/ASME Transactions on Mechatronics
Advanced Vision and Imaging
1.00
article

Adaptive Visual Servoing Control of Closed-Architecture Robots With Both Visibility Constraints and Tracking Error Constraints

Yafeng Li, Keli Pang, Choon Ki Ahn, Changchun Hua, Yu Zhang
article en
4 citations

Abstract

Although there are significant achievements in adaptive visual servoing control of robots with the consideration of visibility constraints or tracking error constraints, most of these approaches are limited to addressing one aspect of the two constraint issues. This article presents a novel adaptive visual servoing control solution specifically designed for robots with closed architecture, effectively managing both visibility constraints and tracking error constraints simultaneously. First, by appropriately combining the image-space position-based constraining function and tracking error-based constraining function, we develop a novel composite barrier function (CBF), enabling the transformation of the visual servoing system with both visibility constraints and tracking error constraints into a functionally equivalent “unconstrained” one. By stabilizing the “unconstrained” system, both visibility constraints and tracking error constraints are handled simultaneously for the first time. Then, we introduce a CBF-based adaptive visual servoing loop controller, designed to dynamically compute the position or velocity commands for the torque control loop. The controller's proven ability to guarantee the convergence of the visual servoing system, without modifying the manufacturer-embedded torque controller, renders it suitable for a wide range of industrial/commercial robots with closed architecture. Experiments are conducted on an industrial manipulator to exhibit the superior capabilities of the method.

IEEE/ASME Transactions on MechatronicsVol. 30(6)
Korea University (KR), Yanshan University (CN), Institute of Electrical Engineering (CN)
National Natural Science Foundation of China, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 22%
Advanced Vision and Imaging
1.00
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.