LDR | | 02901nam u200457 4500 |
001 | | 000000419085 |
005 | | 20190215163409 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
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▼a 9780438126596 |
035 | |
▼a (MiAaPQ)AAI10903053 |
035 | |
▼a (MiAaPQ)umichrackham:001180 |
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▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Goeddel, Robert T. |
245 | 10 |
▼a Policy-Based Planning for Robust Robot Navigation. |
260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2018. |
260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
300 | |
▼a 117 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Adviser: Edwin Olson. |
502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2018. |
520 | |
▼a This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in t |
520 | |
▼a Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this str |
520 | |
▼a DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor env |
520 | |
▼a Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost |
520 | |
▼a This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tas |
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▼a The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering th |
590 | |
▼a School code: 0127. |
650 | 4 |
▼a Computer science. |
650 | 4 |
▼a Robotics. |
690 | |
▼a 0984 |
690 | |
▼a 0771 |
710 | 20 |
▼a University of Michigan.
▼b Computer Science and Engineering. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0127 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000551
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
980 | |
▼a 201812
▼f 2019 |
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▼a ***1012033 |