@conference {Tu2021,
	title = {Graph Convolutional Network based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array},
	booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2021},
	pages = {4252{\textendash}4258},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	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.},
	keywords = {Conferences, Location awareness, Magnetic sensors, Real-time systems, Robot sensing systems, Steel, Training data},
	isbn = {978-1-7281-9078-5},
	issn = {1050-4729},
	doi = {10.1109/ICRA48506.2021.9561340},
	author = {Yuxiao Tu and Guanqi Liang and Tin Lun Lam}
}
