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Cyber-Physical Multi-Robot Systems in a Smart Factory : = A Networked AI Agents Approach.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Cyber-Physical Multi-Robot Systems in a Smart Factory :/
Reminder of title:	A Networked AI Agents Approach.
Author:	Nie, Zixiang.
Description:	1 online resource (177 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380622417

Cyber-Physical Multi-Robot Systems in a Smart Factory : = A Networked AI Agents Approach.
Nie, Zixiang.

Cyber-Physical Multi-Robot Systems in a Smart Factory :A Networked AI Agents Approach. - 1 online resource (177 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--University of South Florida, 2023.

Includes bibliographical references

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization-all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.However, the implementation of MRS introduces new sets of challenges, including the need for a coordination mechanism with superior agility, productivity, and energy efficiency. Further complexities arise in ensuring system resilience under unpredictable production demands and cyber-physical errors, which contribute to the non-stationary nature of smart factories. To address these issues, this dissertation proposes the cyber-physical AI agent system approach. This approach proposed is based on autonomous Artificial Intelligence (AI) agents that emphasize knowledge-based rationality, and AI cognitions including reasoning, prediction, and planning. Additionally, the AI agents learn to adapt to non-stationarity through data-driven computations while interacting with the environment.This dissertation offers both theoretical and applicational contributions to the electrical engineering field. On the theoretical side, it solidates knowledge-based problem-solving of AI research, using graph models as domain models for the AI cognitions. More importantly, when multiple AI agents unite into a system, with wireless information exchange and social learning, the networked AI agents achieve collaboration toward collective objectives through communications. On the practical side, this work addresses real-world challenges in smart factories, including real-time Multi-Robot Task Allocation (MRTA), resilient operation of production robots in the presence of physical errors, predictive path coordination for transportation robots, and maintaining the operational integrity of MRS in the presence of cyber-physical errors.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380622417Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Autonomous AIIndex Terms--Genre/Form:

554714
Electronic books.

Cyber-Physical Multi-Robot Systems in a Smart Factory : = A Networked AI Agents Approach.
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520 $a This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization-all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.However, the implementation of MRS introduces new sets of challenges, including the need for a coordination mechanism with superior agility, productivity, and energy efficiency. Further complexities arise in ensuring system resilience under unpredictable production demands and cyber-physical errors, which contribute to the non-stationary nature of smart factories. To address these issues, this dissertation proposes the cyber-physical AI agent system approach. This approach proposed is based on autonomous Artificial Intelligence (AI) agents that emphasize knowledge-based rationality, and AI cognitions including reasoning, prediction, and planning. Additionally, the AI agents learn to adapt to non-stationarity through data-driven computations while interacting with the environment.This dissertation offers both theoretical and applicational contributions to the electrical engineering field. On the theoretical side, it solidates knowledge-based problem-solving of AI research, using graph models as domain models for the AI cognitions. More importantly, when multiple AI agents unite into a system, with wireless information exchange and social learning, the networked AI agents achieve collaboration toward collective objectives through communications. On the practical side, this work addresses real-world challenges in smart factories, including real-time Multi-Robot Task Allocation (MRTA), resilient operation of production robots in the presence of physical errors, predictive path coordination for transportation robots, and maintaining the operational integrity of MRS in the presence of cyber-physical errors.
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