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Algorithms for Multi-Agent Robotic Assembly Planning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithms for Multi-Agent Robotic Assembly Planning./
作者:	Brown, Kyle Jordan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	196 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Manufacturing. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688352
ISBN:	9798544203971

Algorithms for Multi-Agent Robotic Assembly Planning.
Brown, Kyle Jordan.

Algorithms for Multi-Agent Robotic Assembly Planning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 196 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

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

This thesis presents algorithms for multi-agent robotic assembly planning in automated manufacturing contexts. Our work touches on many pieces of the "factory autonomy stack." The first contribution of the thesis is the introduction of a discrete factory problem formulation that includes time-extended multi robot task allocation, inter-task precedence constraints, and collision-avoidance constraints. We propose an efficient method to solve such problems. The two keys to our algorithm's efficiency are its decoupling of task assignment and route planning, and its ability to take advantage of situations where some robots can be delayed in their own schedule without causing any negative impact on the factory's overall performance. The next major contribution of the thesis is a family of replanning algorithms geared toward an online version of our discrete factory problem. In the online setting, the factory command center periodically receives new manufacturing workloads that must be quickly incorporated into the overall schedule. We show through an extensive set of experiments that our replanning methods are applicable across a wide range of problems. Moreover, our proposed methods can be applied in such a way that the factory will never have to freeze while waiting to receive an updated plan. Our final contribution is a proof-of-concept system for large scale multi-robot assembly planning with assemblies and raw materials of arbitrary shapes and sizes. Our system starts with raw materials and a bare-bones set of instructions about how those materials fit together. The planner then synthesizes a construction plan, which defines how each payload will be carried (by one or more robots), where each assembly and subassembly will be built, and which specific robots will be assigned to each solo and collaborative transport task. Finally, a reactive collision-avoidance control policy enables the robots to execute the construction plan in a distributed manner. We demonstrate in simulation that our system can synthesize---in minutes---construction plans for assemblies with hundred of components. Though we do not address all of the relevant ``real-world'' considerations that surround multi robot manufacturing, our work is a small step toward large scale automated construction with mobile robots.

ISBN: 9798544203971Subjects--Topical Terms:

3389707
Manufacturing.

Algorithms for Multi-Agent Robotic Assembly Planning.
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