@conference {Liang2022,
	title = {Energy Sharing Mechanism for a Freeform Robotic System - FreeBOT},
	booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
	year = {2022},
	pages = {4232{\textendash}4238},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Philadelphia, PA, USA},
	abstract = {Energy sharing in modular self-reconfigurable robots ensures the energy balance of the modules, thus allowing the system to work sustainably. This paper proposes an energy sharing mechanism for a novel modular self-reconfigurable robot that allows free connections among modules, termed as FreeBOT, such that each FreeBOT can share energy with peers through surface contact. Corresponding energy sharing rules are proposed to achieve an energy sharing network structure without invalid components. As alternative choices, several types of networks subjected to the above requirements are provided, which also maximize the number of FreeBOTs joining to share energy. We implement and test the prototype of the energy sharing mechanism on FreeBOT. The experimental results show that the mechanism can effectively achieve energy sharing among FreeBOTs.},
	keywords = {Automation, Prototypes, Robots},
	isbn = {978-1-7281-9682-4},
	doi = {10.1109/ICRA46639.2022.9811860},
	author = {Guanqi Liang and Yuxiao Tu and Lijun Zong and Junfeng Chen and Tin Lun Lam}
}
@article {Guo2021,
	title = {Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment},
	journal = {IEEE Robotics and Automation Letters},
	volume = {6},
	year = {2021},
	pages = {8349{\textendash}8356},
	publisher = {IEEE},
	abstract = {The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this letter, we propose a semantic histogram based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95\% for global localization, while the accuracy of the state-of-the-art method is 85\%.},
	keywords = {Global localization, Histograms, large viewpoint difference, Location awareness, multi-robot map merging, Real-time systems, semantic SLAM, Semantics, Simultaneous localization and mapping, Three-dimensional displays, Visualization},
	issn = {2377-3774},
	doi = {10.1109/LRA.2021.3058935},
	author = {Xiyue Guo and Junjie Hu and Junfeng Chen and Fuqin Deng and Tin Lun Lam}
}
