Decentralized control of internal wrenches in a multi-manipulators object transportation task
(2025) p.705-710- Abstract
- This paper presents a decentralized strategy for a team of N robotic manipulators cooperatively grasping and manipulating an object. A two-step strategy has been designed. In the first step, each robot runs N − 1 consensus-based estimators to estimates the wrenches applied to the object by its teammates even when direct all-to-all communication is unavailable. In the second step, each manipulator runs a local compliance controller to adjust its end-effector compliance, thereby reducing internal stresses and preventing potential damage to the object. The effectiveness of the proposed scheme is validated through simulations of a work-cell with four 7-DOFs manipulators by using a realistic dynamic simulator. Simulation results confirm the... (More)
- This paper presents a decentralized strategy for a team of N robotic manipulators cooperatively grasping and manipulating an object. A two-step strategy has been designed. In the first step, each robot runs N − 1 consensus-based estimators to estimates the wrenches applied to the object by its teammates even when direct all-to-all communication is unavailable. In the second step, each manipulator runs a local compliance controller to adjust its end-effector compliance, thereby reducing internal stresses and preventing potential damage to the object. The effectiveness of the proposed scheme is validated through simulations of a work-cell with four 7-DOFs manipulators by using a realistic dynamic simulator. Simulation results confirm the capability of the approach to limit internal wrenches, even under restricted communication conditions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/eae3f3b0-e5d5-4949-9d0a-434636c4e452
- author
- Carriero, Graziano
; Sileo, Monica
; Karayiannidis, Yiannis
LU
; Pierri, Francesco
and Caccavale, Fabrizio
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Simulation, Internal stresses, Decentralized control, Transportation, Europe, Grasping, End effectors, Manipulator dynamics
- host publication
- 2025 European Control Conference (ECC)
- pages
- 6 pages
- DOI
- 10.23919/ECC65951.2025.11187074
- project
- Intelligent trajectory predictions at sea using neural ordinary differential equations
- language
- Unknown
- LU publication?
- yes
- id
- eae3f3b0-e5d5-4949-9d0a-434636c4e452
- date added to LUP
- 2025-10-19 18:39:38
- date last changed
- 2025-10-25 03:36:18
@inproceedings{eae3f3b0-e5d5-4949-9d0a-434636c4e452,
abstract = {{This paper presents a decentralized strategy for a team of N robotic manipulators cooperatively grasping and manipulating an object. A two-step strategy has been designed. In the first step, each robot runs N − 1 consensus-based estimators to estimates the wrenches applied to the object by its teammates even when direct all-to-all communication is unavailable. In the second step, each manipulator runs a local compliance controller to adjust its end-effector compliance, thereby reducing internal stresses and preventing potential damage to the object. The effectiveness of the proposed scheme is validated through simulations of a work-cell with four 7-DOFs manipulators by using a realistic dynamic simulator. Simulation results confirm the capability of the approach to limit internal wrenches, even under restricted communication conditions.}},
author = {{Carriero, Graziano and Sileo, Monica and Karayiannidis, Yiannis and Pierri, Francesco and Caccavale, Fabrizio}},
booktitle = {{2025 European Control Conference (ECC)}},
keywords = {{Simulation; Internal stresses; Decentralized control; Transportation; Europe; Grasping; End effectors; Manipulator dynamics}},
language = {{und}},
pages = {{705--710}},
title = {{Decentralized control of internal wrenches in a multi-manipulators object transportation task}},
url = {{http://dx.doi.org/10.23919/ECC65951.2025.11187074}},
doi = {{10.23919/ECC65951.2025.11187074}},
year = {{2025}},
}