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Efficient and asymptotically optimal kinodynamic motion planning

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TitleInfo
Title
Efficient and asymptotically optimal kinodynamic motion planning
Name (type = personal)
NamePart (type = family)
Littlefield
NamePart (type = given)
Zakary
NamePart (type = date)
1989-
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Zakary Littlefield
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author
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Kostas E
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Kostas E Bekris
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Advisory Committee
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chair
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Boularias
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Abdeslam
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Abdeslam Boularias
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Advisory Committee
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internal member
Name (type = personal)
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Yu
NamePart (type = given)
Jingjin
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Jingjin Yu
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Advisory Committee
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internal member
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Barfoot
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Timoty
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Timoty Barfoot
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Advisory Committee
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outside member
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Rutgers University
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degree grantor
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School of Graduate Studies
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school
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theses
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2020
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2020-05
Language
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English
Abstract (type = abstract)
This dissertation explores properties of motion planners that build tree data structures in a robot’s state space. Sampling-based tree planners are especially useful for planning for systems with significant dynamics, due to the inherent forward search that is per- formed. This is in contrast to roadmap planners that require a steering local planner in order to make a graph containing multiple possible paths. This dissertation explores a family of motion planners for systems with significant dynamics, where a steering local planner may be computationally expensive or may not exist. These planners focus on providing practical path quality guarantees without prohibitive computational costs. These planners can be considered successors of each other, in that each sub- sequent algorithm addresses some drawback of its predecessor. The first algorithm, Sparse-RRT, addresses a drawback of the RRT method by considering path quality during the tree construction process. Sparse-RRT is proven to be probabilistically complete under mild conditions for the first time here, albeit with a poor convergence rate. The second algorithm presented, SST, provides probabilistic completeness and asymptotic near-optimality properties that are provable, but at the cost of additional algorithmic overhead. SST is shown to improve the convergence rate compared to Sparse-RRT. The third algorithm, DIRT, incorporates learned lessons from these two algorithms and their shortcomings, incorporates task space heuristics to further improve runtime performance, and simplifies the parameters to more user-friendly ones. DIRT is also shown to be probabilistically complete and asymptotically near-optimal. Application areas explored using this family of algorithms include evaluation of distance functions for planning in belief space, manipulation in cluttered environments, and locomotion planning for an icosahedral tensegrity-based rover prototype that requires a physics engine to simulate its motions.
Subject (authority = LCSH)
Topic
Robots -- Control systems
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_10590
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1 online resource (xv, 116 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-kgnt-7h03
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Littlefield
GivenName
Zakary
Role
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RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-02-22 19:39:50
AssociatedEntity
Name
Zakary Littlefield
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2020-02-22T19:14:51
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2020-02-22T19:14:51
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