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Configuration and Motion Planning Using Graph Augmentation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Configuration and Motion Planning Using Graph Augmentation./
作者:	Wang, Xiaolong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	195 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29063738
ISBN:	9798802700792

Configuration and Motion Planning Using Graph Augmentation.
Wang, Xiaolong.

Configuration and Motion Planning Using Graph Augmentation. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 195 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Lehigh University, 2022.

This item must not be sold to any third party vendors.

Search-based techniques have been widely used in robot path planning for finding trajectories in configuration spaces. Despite advantages such as completeness, optimality, robustness, and efficiency, even in complex environments, under many circumstances, a search in the configuration space alone is not sufficient for solving problems with complex constraints. To solve such problems meaningfully, it may be required to augment the vertices of the search graph with additional information that represents specific traits of the solution itself, representing an abstraction of a larger configuration-space or state-space of a larger, more complex system. We refer to a configuration graph integrated with such additional information as an augmented graph. Once constructed, it is convenient to compute solutions due to the general nature of graph search algorithms. Depending on the specific problem, a variety of additional state information can be integrated to construct an augmented graph.Motivated by specific problems, in this thesis, we propose a few distinct constructions of augmented graphs and their corresponding applications. For a snake-like robot, a contact-augmented graph is proposed to facilitate the configuration planning that allows the robot to interact with static obstacles, plan stable configurations, and locomote itself. A topology-augmented graph is used for mobile robot systems. Building on this idea, we introduce algorithms for cable-driven robots in cluttered environments that enable them to plan and optimize their workspace, plan paths and execute tasks within that workspace using a task-augmented graph. A similar technique based on topology-augmented graphs is used to minimize traffic congestion for a coordination-free multi-robot system in an environment with uncertainties. Finally, a Reeb structure constructed from a configuration graph is shown to help reduce the dimensionality of search space for articulated robots, thus enabling faster motion planning than searching in a complete high-dimensional configuration space.

ISBN: 9798802700792Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Collision avoidance

Configuration and Motion Planning Using Graph Augmentation.
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245 1 0 $a Configuration and Motion Planning Using Graph Augmentation.
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300 $a 195 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Bhattacharya, Subhrajit.
502 $a Thesis (Ph.D.)--Lehigh University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Search-based techniques have been widely used in robot path planning for finding trajectories in configuration spaces. Despite advantages such as completeness, optimality, robustness, and efficiency, even in complex environments, under many circumstances, a search in the configuration space alone is not sufficient for solving problems with complex constraints. To solve such problems meaningfully, it may be required to augment the vertices of the search graph with additional information that represents specific traits of the solution itself, representing an abstraction of a larger configuration-space or state-space of a larger, more complex system. We refer to a configuration graph integrated with such additional information as an augmented graph. Once constructed, it is convenient to compute solutions due to the general nature of graph search algorithms. Depending on the specific problem, a variety of additional state information can be integrated to construct an augmented graph.Motivated by specific problems, in this thesis, we propose a few distinct constructions of augmented graphs and their corresponding applications. For a snake-like robot, a contact-augmented graph is proposed to facilitate the configuration planning that allows the robot to interact with static obstacles, plan stable configurations, and locomote itself. A topology-augmented graph is used for mobile robot systems. Building on this idea, we introduce algorithms for cable-driven robots in cluttered environments that enable them to plan and optimize their workspace, plan paths and execute tasks within that workspace using a task-augmented graph. A similar technique based on topology-augmented graphs is used to minimize traffic congestion for a coordination-free multi-robot system in an environment with uncertainties. Finally, a Reeb structure constructed from a configuration graph is shown to help reduce the dimensionality of search space for articulated robots, thus enabling faster motion planning than searching in a complete high-dimensional configuration space.
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653 $a Configuration planning
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653 $a Multi-robot systems
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