@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}
}
@conference {Tu2022,
	title = {FreeSN: A Freeform Strut-node Structured Modular Self-reconfigurable Robot - Design and Implementation},
	booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
	year = {2022},
	pages = {4239{\textendash}4245},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Philadelphia, PA, USA},
	abstract = {This paper proposes a novel freeform strut-node structured modular self-reconfigurable robot (MSRR) called FreeSN, consisting of strut and node modules. A node module is mainly a low-carbon steel spherical shell. A strut module contains two freeform connectors, which provide strong magnetic connections and flexible spherical motions. The FreeSN system shares the benefits of freeform connection and strut-node structures. The freeform connection brings good adaptability to the environment. The triangle substructures inside the system configuration significantly improve the structural stability. The parallel execution of module motions can superpose the module capabilities and makes the system more scalable. The modules can combine these robot features by selecting the system configuration and better fit different circumstances and tasks. Four demonstrations, including assembly, obstacle crossing, transportation, and object manipulation, are designed to show the capabilities of the FreeSN system in different aspects. The results show the great performance and versatility of this MSRR system.},
	keywords = {Automation, Connectors, Robots, Steel, Structural engineering, Task analysis, Transportation},
	isbn = {978-1-7281-9682-4},
	doi = {10.1109/ICRA46639.2022.9811583},
	author = {Yuxiao Tu and Guanqi Liang and Tin Lun Lam}
}
@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}
}
@conference {Liang2020,
	title = {FreeBOT: A Freeform Modular Self-reconfigurable Robot with Arbitrary Connection Point - Design and Implementation},
	booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2020},
	pages = {6506{\textendash}6513},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV, USA},
	abstract = {This paper proposes a novel modular selfreconfigurable robot (MSRR) "FreeBOT", which can be connected freely at any point on other robots. FreeBOT is mainly composed of two parts: a spherical ferromagnetic shell and an internal magnet. The connection between the modules is genderless and instant, since the internal magnet can freely attract other FreeBOT spherical ferromagnetic shells, and not need to be precisely aligned with the specified connector. This connection method has fewer physical constraints, so the FreeBOT system can be extended to more configurations to meet more functional requirements. FreeBOT can accomplish multiple tasks although it only has two motors: module independent movement, connector management and system reconfiguration. FreeBOT can move independently on the plane, and even climb on ferromagnetic walls; a group of FreeBOTs can traverse complex terrain. Numerous experiments have been conducted to test its function, which shows that the FreeBOT system has great potential to realize a freeform robotic system.},
	keywords = {Connectors, Intelligent robots, Task analysis},
	isbn = {978-1-7281-6213-3},
	issn = {2153-0858},
	doi = {10.1109/IROS45743.2020.9341129},
	author = {Guanqi Liang and Haobo Luo and Ming Li and Huihuan Qian and Tin Lun Lam}
}
@conference {Li2020,
	title = {Robot-to-Robot Relative Pose Estimation based on Semidefinite Relaxation Optimization},
	booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2020},
	pages = {4491{\textendash}4498},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV, USA},
	abstract = {In this paper, the 2D robot-to-robot relative pose (position and orientation) estimation problem based on ego-motion and noisy distance measurements is considered. We address this problem using an optimization-based method, which does not require complicated numerical analysis while yields no inferior relative localization (RL) results compared to existing approaches. In particular, we start from a state-of-the-art method named square distances weighted least square (SD-WLS), and reformulate it as a non-convex quadratically constrained quadratic programming (QCQP) problem. To handle its non-convex nature, a semidefinite programming (SDP) relaxation optimization-based method is proposed, and we prove that the relaxation is tight when measurements are free from noise or just corrupted by small noise. Further, to obtain the optimal solution of the relative pose estimation problem in the sense of maximum likelihood estimation (MLE), a theoretically optimal WLS method is developed to refine the estimate from the SDP optimization. Comprehensive simulations and well-designed experiments are presented for validating the tightness of the SDP relaxation, and the effectiveness of the proposed algorithm is highlighted by comparing it to the existing approaches.},
	keywords = {Maximum likelihood estimation, Noise measurement, Optimization, Particle measurements, Pose estimation, Quadratic programming, Two dimensional displays},
	isbn = {978-1-7281-6213-3},
	issn = {2153-0858},
	doi = {10.1109/IROS45743.2020.9341568},
	author = {Ming Li and Guanqi Liang and Haobo Luo and Huihuan Qian and Tin Lun Lam}
}
