@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 {Zhang2021,
	title = {Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation},
	booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2021},
	pages = {8158{\textendash}8164},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	abstract = {This paper introduces a novel task-space decomposed motion planning framework for multi-robot simultaneous locomotion and manipulation. When several manipulators hold an object, closed-chain kinematic constraints are formed, and it will make the motion planning problems challenging by inducing lower-dimensional singularities. Unfortunately, the constrained manifold will be even more complicated when the manipulators are equipped with mobile bases. We address the problem by introducing a dual-resolution motion planning framework which utilizes a convex task region decomposition method, with each resolution tuned to efficient computation for their respective roles. Concretely, this dual-resolution approach enables a global planner to explore the low-dimensional decomposed task-space regions toward the goal, then a local planner computes a path in high-dimensional constrained configuration space. We demonstrate the proposed method in several simulations, where the robot team transports the object toward the goal in the obstacle-rich environments.},
	keywords = {Computational modeling, Conferences, Kinematics, Manifolds, Manipulators, Planning, System recovery},
	isbn = {978-1-7281-9078-5},
	issn = {1050-4729},
	doi = {10.1109/ICRA48506.2021.9560902},
	author = {Xiaoyu Zhang and Lei Yan and Tin Lun Lam and Sethu Vijayakumar}
}
