東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Distributed ,and Certifiable Optimiz...

Halsted, Trevor Neill.

FindBook

Google Book

Amazon

博客來

Distributed ,and Certifiable Optimization for Multi-Robot Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed ,and Certifiable Optimization for Multi-Robot Systems./
作者:	Halsted, Trevor Neill.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	180 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Robots. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31049694
ISBN:	9798382637587

Distributed ,and Certifiable Optimization for Multi-Robot Systems.
Halsted, Trevor Neill.

Distributed ,and Certifiable Optimization for Multi-Robot Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 180 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

This thesis addresses two key challenges in applying optimization methods to multi-robot systems. First, the need for Distributed Optimization arises in unstructured and remote environments in which centralized coordination is impractical or infeasible. Second, the nonconvexity of many relevant problems necessitates certifiable optimization, which provides provable bounds on the quality of a solution. We present new techniques for both distributed optimization and certifiable optimization, as well as a unified framework for distributed and certifiable multi-robot optimization.Several tools presented in this thesis enable robots to achieve system-wide coordination through pairwise communication. The ability to cooperatively solve optimization problems through distributed robot-to-robot communication is a crucial component of multi-robot autonomy. We introduce a communication-efficient general-purpose distributed optimization method, Separable Optimization Variable ADMM, and demonstrate its scalability and robustness. Additionally, an optimization-based approach to distributed estimation replicates the performance of the centralized Kalman Filter. We prove that our Distributed Rolling-Window Tracking algorithm maintains consistent estimates without communicating covariance matrices as alternative methods require.Our approach to certifiable optimization considers a broad class of synchronization problems that admit a semidefinite relaxation. We present a novel reformulation of the distance-based localization problem, showing for the first time that it belongs to this same class of certifiable optimization problems. Compared to other distance-based localization algorithms, our method recovers good solutions over a range of problem instantiations.Finally, we present a new approach to distributed certifiable optimization. Our technique for solving a decomposition of a sparse semidefinite program introduces a novel "dualized agreement" constraint. This formulation allows networked robots to iteratively solve for their individual components in a large semidefinite program. Together, these contributions provide a general framework for fully distributed and provably optimalmulti-robot coordination.

ISBN: 9798382637587Subjects--Topical Terms:

529507
Robots.

Distributed ,and Certifiable Optimization for Multi-Robot Systems.
LDR:03263nmm a2200349 4500 001 2402956
005 20241104055828.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798382637587
035 $a (MiAaPQ)AAI31049694
035 $a (MiAaPQ)STANFORDvp445pw4013
035 $a AAI31049694
040 $a MiAaPQ $c MiAaPQ
100 1 $a Halsted, Trevor Neill. $3 3773218
245 1 0 $a Distributed ,and Certifiable Optimization for Multi-Robot Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 180 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Schwager, Mac.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
520 $a This thesis addresses two key challenges in applying optimization methods to multi-robot systems. First, the need for Distributed Optimization arises in unstructured and remote environments in which centralized coordination is impractical or infeasible. Second, the nonconvexity of many relevant problems necessitates certifiable optimization, which provides provable bounds on the quality of a solution. We present new techniques for both distributed optimization and certifiable optimization, as well as a unified framework for distributed and certifiable multi-robot optimization.Several tools presented in this thesis enable robots to achieve system-wide coordination through pairwise communication. The ability to cooperatively solve optimization problems through distributed robot-to-robot communication is a crucial component of multi-robot autonomy. We introduce a communication-efficient general-purpose distributed optimization method, Separable Optimization Variable ADMM, and demonstrate its scalability and robustness. Additionally, an optimization-based approach to distributed estimation replicates the performance of the centralized Kalman Filter. We prove that our Distributed Rolling-Window Tracking algorithm maintains consistent estimates without communicating covariance matrices as alternative methods require.Our approach to certifiable optimization considers a broad class of synchronization problems that admit a semidefinite relaxation. We present a novel reformulation of the distance-based localization problem, showing for the first time that it belongs to this same class of certifiable optimization problems. Compared to other distance-based localization algorithms, our method recovers good solutions over a range of problem instantiations.Finally, we present a new approach to distributed certifiable optimization. Our technique for solving a decomposition of a sparse semidefinite program introduces a novel "dualized agreement" constraint. This formulation allows networked robots to iteratively solve for their individual components in a large semidefinite program. Together, these contributions provide a general framework for fully distributed and provably optimalmulti-robot coordination.
590 $a School code: 0212.
650 4 $a Robots. $3 529507
650 4 $a Decomposition. $3 3561186
650 4 $a Automobiles. $3 560291
650 4 $a Convex analysis. $3 3681761
650 4 $a Semidefinite programming. $3 1047883
650 4 $a Communication. $3 524709
650 4 $a Optimization techniques. $3 3681622
650 4 $a Autonomous vehicles. $3 2179092
650 4 $a Mathematics. $3 515831
650 4 $a Robotics. $3 519753
650 4 $a Transportation. $3 555912
690 $a 0459
690 $a 0405
690 $a 0771
690 $a 0709
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0212
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31049694