@article {Luo2022,
	title = {Adaptive Flow Planning of Modular Spherical Robot Considering Static Gravity Stability},
	journal = {IEEE Robotics and Automation Letters},
	volume = {7},
	year = {2022},
	pages = {4228{\textendash}4235},
	publisher = {IEEE},
	abstract = {Modular robots have a unique obstacle-crossing method, flow. Flow is realized by constantly changing the connection relationship between modules, namely reconfiguration. Existing flow planning methods do not consider the static stability in their adaptation to obstacles. This letter proposes a flow planning method with scalability, adaptability, and static gravity stability. The criterion of static gravity stability is always satisfied through the following two innovations. First, each target configuration in the flow process is designed to grasp obstacles like vines. Second, in motion planning, each module maintains contact with the obstacle or a fixed module to maximize the supporting polygon of the configuration. What{\textquoteright}s more, the simplified path output by the connection planning and the precise calculation based on the mesh model realize the scalability and adaptability of the flow planning method. In simulation, we evaluate the adaptability to various obstacles and the margin of static gravity stability.},
	keywords = {Cellular and modular robots, Gravity, Hardware, path planning for multiple mobile robots or agents, Planning, Robot kinematics, Robots, scheduling and coordination, Stability criteria, Three-dimensional displays},
	issn = {2377-3774},
	doi = {10.1109/LRA.2022.3150028},
	author = {Haobo Luo 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 {Luo2020,
	title = {An Obstacle-crossing Strategy Based on the Fast Self-reconfiguration for Modular Sphere Robots},
	booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2020},
	pages = {3296{\textendash}3303},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV, USA},
	abstract = {This paper introduces an obstacle-crossing strategy, and the self-reconfiguration algorithm for a new class of modular robots called the rolling sphere, which can fit obstacles represented by cubes of different sizes due to the chain connection of multiple spheres. For the self-reconfiguration of the rolling spheres, a large gradient is obtained by classifying its action types and hierarchically minimizing the distance between the initial configuration and the final configuration. The most direct use of this large gradient is the fast crossing of various obstacles, by jointing multiple self-reconfigurations according to the OctoMap of the obstacles. It is verified in simulation that the self-reconfiguration takes full advantage of the parallel movement of multiple modules to reduce the total time steps, and the obstacle-crossing strategy can adapt to a variety of obstacles.},
	keywords = {Adaptation models, Classification algorithms, Intelligent robots},
	isbn = {978-1-7281-6213-3},
	issn = {2153-0858},
	doi = {10.1109/IROS45743.2020.9341162},
	author = {Haobo Luo and Ming Li and Guangqi Liang 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}
}
