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Compact Representations for Efficien...
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Dobson, Andrew.
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Compact Representations for Efficient Robot Motion Planning with Formal Guarantees.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Compact Representations for Efficient Robot Motion Planning with Formal Guarantees./
Author:
Dobson, Andrew.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:
172 p.
Notes:
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:
Dissertation Abstracts International79-08B(E).
Subject:
Computer science. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10799896
ISBN:
9780355780901
Compact Representations for Efficient Robot Motion Planning with Formal Guarantees.
Dobson, Andrew.
Compact Representations for Efficient Robot Motion Planning with Formal Guarantees.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 172 p.
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2017.
This work provides compact representations for single- and multi-robot motion planning in the context of prehensile robot manipulation. This work describes the asymptotic near-optimality and probabilistic near-optimality properties of these planners. Probabilistic near-optimality is leveraged to provide practical and grounded stopping criteria for these methods which probabilistically guarantee the methods return high-quality paths. It is also shown how these methods can be leveraged to produce a compact planning representation, which is a lightweight structure that is quick to query and easy to store. The work also outlines a compact representation for solving multi-arm manipulation tasks, and integrates a scalable, asymptotically optimal multi-robot motion planning method to provide scalable, globally asymptotically optimal task and motion planning in manipulation domains.
ISBN: 9780355780901Subjects--Topical Terms:
523869
Computer science.
Compact Representations for Efficient Robot Motion Planning with Formal Guarantees.
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This work provides compact representations for single- and multi-robot motion planning in the context of prehensile robot manipulation. This work describes the asymptotic near-optimality and probabilistic near-optimality properties of these planners. Probabilistic near-optimality is leveraged to provide practical and grounded stopping criteria for these methods which probabilistically guarantee the methods return high-quality paths. It is also shown how these methods can be leveraged to produce a compact planning representation, which is a lightweight structure that is quick to query and easy to store. The work also outlines a compact representation for solving multi-arm manipulation tasks, and integrates a scalable, asymptotically optimal multi-robot motion planning method to provide scalable, globally asymptotically optimal task and motion planning in manipulation domains.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10799896
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