@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}
}
@conference {36,
	title = {Coverage search in 3D},
	year = {2013},
	address = {Linkoeping, Sweden},
	abstract = {Searching with a sensor for objects and to observe parts of a known environment efficiently is a fundamental problem in many real-world robotic applications such as household robots searching for objects, inspection robots searching for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of identifying and planning efficient view point sequences for covering complex 3d environments. We compare empirically several variants of our algorithm that allow to trade-off schedule computation against execution time. Our results demonstrate that, despite the intractability of the overall problem, computing effective solutions for coverage search in real 3d environments is feasible.},
	keywords = {3D environments, coverage search, Educational institutions, execution time, household robots, image sequences, inspection robots, leaking pipelines search, objects search, path planning, Planning, rescue robots, Robot sensing systems, robot vision, robotic applications, schedule computation, Search problems, sensor, service robots, solid modelling, survivors search, Three-dimensional displays, Traveling salesman problems, view point sequences},
	doi = {10.1109/SSRR.2013.6719340},
	author = {Dornhege, C. and Kleiner, A. and Kolling, A.}
}
@conference {37,
	title = {Coverage search in 3D},
	booktitle = {Proc. of the {IEEE} Int. Workshop on Safety, Security and Rescue Robotics {(SSRR)},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Linkoeping, Sweden},
	abstract = {Searching with a sensor for objects and to observe parts of a known environment efficiently is a fundamental problem in many real-world robotic applications such as household robots searching for objects, inspection robots searching for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of identifying and planning efficient view point sequences for covering complex 3d environments. We compare empirically several variants of our algorithm that allow to trade-off schedule computation against execution time. Our results demonstrate that, despite the intractability of the overall problem, computing effective solutions for coverage search in real 3d environments is feasible.},
	keywords = {3D environments, coverage search, Educational institutions, execution time, household robots, image sequences, inspection robots, leaking pipelines search, objects search, path planning, Planning, rescue robots, Robot sensing systems, robot vision, robotic applications, schedule computation, Search problems, sensor, service robots, solid modelling, survivors search, Three-dimensional displays, Traveling salesman problems, view point sequences},
	doi = {10.1109/SSRR.2013.6719340},
	author = {Dornhege, C. and Kleiner, A. and Kolling, A.}
}
