Configuration Identification for a Freeform Modular Self-Reconfigurable Robot - FreeSN

Modular self-reconfigurable robotic systems are potentially more robust and adaptive than conventional systems. This article proposes a novel freeform and truss-structured modular self-reconfigurable robot called FreeSN, containing node and strut modules. A node module contains a low-carbon steel sp...

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Vydáno v:IEEE transactions on robotics Ročník 39; číslo 6; s. 1 - 17
Hlavní autoři: Tu, Yuxiao, Lam, Tin Lun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Automation
Cellular and modular robots
Closed loops
Configurations
Connectors
Feedback control
Ferromagnetic materials
graph convolutional network (GCN)
Identification systems
Localization
Location awareness
Low carbon steels
Magnetic levitation
magnetic localization
Modular structures
Modular systems
Modules
Moments of inertia
Nodes
Reconfiguration
Robot sensing systems
Robots
self-reconfigurable
Sensor arrays
sensor fusion
Spherical shells
Steel
Struts
Topology
Wheels
ISSN:1552-3098, 1941-0468
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Shrnutí:Modular self-reconfigurable robotic systems are potentially more robust and adaptive than conventional systems. This article proposes a novel freeform and truss-structured modular self-reconfigurable robot called FreeSN, containing node and strut modules. A node module contains a low-carbon steel spherical shell. A strut module contains two magnetic-based freeform connectors, which can connect to any position of the node module and provide spherical motions. Accurate configuration identification is essential for the automation of modular robot systems. This article presents a novel configuration identification system for FreeSN, including connection point magnetic localization, module identification, module orientation fusion, and system configuration fusion. A magnetic sensor array is integrated into the node module. A graph convolutional network-based magnetic localization algorithm is proposed, which can efficiently locate a variable number of magnet arrays under ferromagnetic material distortion. The module relative orientation is then estimated by fusing the magnetic localization result with the inertia moment unit and wheel odometry. Finally, the system configuration can be estimated, including the connection topology graph and the poses of modules. The configuration identification system is validated by a series of accuracy evaluation experiments and two library-based automation demonstrations based on closed-loop control.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3303848