2020
DOI: 10.1177/0959651820932760
|View full text |Cite
|
Sign up to set email alerts
|

Motion control of a cable-suspended robot using image recognition with coordinate transformation

Abstract: This study will concern the implementation of a vision-based controller for cable-suspended robots, and especially for the pick-and-place function. This work will develop a novel algorithm that combines cable-suspended robot coordinate transformation with image recognition to manipulate the suspended gripper to the desired position for material-handling tasks. Two webcams sense the position of the end-effector; one will be used to calculate the horizontal planar coordinate of the target, and the other… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 34 publications
(89 reference statements)
0
1
0
Order By: Relevance
“…23,24 Moreover, the robot was affected by various uncertain factors, such as inaccurate parameters, unknown load, nonlinear elastic force and friction, which made it difficult to obtain accurate dynamics of the robot. 25,26 Thus, some model-free control methods were used to overcome uncertainty of the system. [27][28][29][30] A new adaptive control scheme was proposed utilizing sliding-mode control for the nonlinear systems.…”
Section: Introductionmentioning
confidence: 99%
“…23,24 Moreover, the robot was affected by various uncertain factors, such as inaccurate parameters, unknown load, nonlinear elastic force and friction, which made it difficult to obtain accurate dynamics of the robot. 25,26 Thus, some model-free control methods were used to overcome uncertainty of the system. [27][28][29][30] A new adaptive control scheme was proposed utilizing sliding-mode control for the nonlinear systems.…”
Section: Introductionmentioning
confidence: 99%