[Presentation] Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array 

Authors: Yuxiao Tu, Guanqi Liang, and Tin Lun Lam
Corresponding author: Tin Lun Lam (Email: tllam@cuhk.edu.cn; Website: sites.google.com/site/lamtinlun ​)
Published in: 2021 IEEE International Conference on Robotics and Automation (ICRA), Xian, China, May 30 - June 5, 2021.
Freeform Robotics: freeformrobotics.org
Robotics & AI Lab CUHKSZ: rail.cuhk.edu.cn
Title:
Graph Convolutional Network Based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array
Abstract:
Modular self-reconfigurable robotic (MSRR) systems are potentially more robust and more adaptive than conventional systems. Following our previous work where we proposed a freeform MSRR module called FreeBOT, this paper presents a novel configuration detection system for FreeBOT using a magnetic sensor array. A FreeBOT module can be connected by up to 11 modules, and the proposed configuration detection system can locate a variable number of connection points accurately in real-time. By equipping FreeBOT with 24 magnetic sensors, the magnetic field density produced by magnets and steel spherical shells can be monitored. The connectable area is split into 199 non-uniform regions, including 84 uniform regions. Using a Graph Convolutional Network (GCN) based algorithm, the connection points can be located accurately under ferromagnetic environments. The system can locate a variable number of connection points for such a region division with only single connection point training data. Finally, the localization algorithm can run faster than 40 Hz on FreeBOT. With the real-time configuration detection system, the FreeBOT system has the potential to reconfigure automatically and accurately.

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