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Metareasoning for Robots

Adapting in Dynamic and Uncertain Environments

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  • © 2023

Overview

  • Introduces key concepts, discusses design options for metareasoning approaches and policies
  • Features design guidelines and helpful examples that researchers and engineers can use to make their robots smarter
  • Adopts a systems engineering perspective in which metareasoning can improve the overall robot or autonomous system

Part of the book series: Synthesis Lectures on Computer Science (SLCS)

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About this book

This book is a state of the art resource that robotics researchers and engineers can use to make their robots and autonomous vehicles smarter. Readers will be able to describe metareasoning, select an appropriate metareasoning approach, and synthesize metareasoning policies. Metareasoning for Robots adopts a systems engineering perspective in which metareasoning is an approach that can improve the overall robot or autonomous system, not just one component or subsystem. This book introduces key concepts, discusses design options for metareasoning approaches and policies, and presents approaches for testing and evaluation of metareasoning policies. After considering the conceptual design phase, it discusses how to implement metareasoning in the robot’s software architecture and how to synthesize metareasoning policies.  Every chapter has references to valuable works on robotics and metareasoning, and the book uses examples from the author’s own research andfrom other research groups to illustrate these ideas. In addition, this book provides links to books and papers for readers who wish to investigate these topics further.  


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Keywords

Table of contents (5 chapters)

Reviews

“This is a comprehensive volume of tutorial notes on metareasoning for all kinds of robotics-based systems. It should interest readers working in robotic data analytics. … This study of metareasoning in robotics is a fairly easy read.” (Anoop Malaviya, Computing Reviews, December 20, 2023)

Authors and Affiliations

  • University of Maryland, College Park, USA

    Jeffrey W. Herrmann

About the author

Jeffrey W. Herrmann, PhD., is a Professor at the University of Maryland, where he holds a joint appointment with the Department of Mechanical Engineering and the Institute for Systems Research. He is a member of Society of Catholic Scientists, IISE, ASME, and the Design Society. He is an associate editor for the ASME Journal of Autonomous Vehicles and Systems. In 2012 he and Gino Lim were the conference chairs for the Industrial and Systems Engineering Research Conference.Dr. Herrmann earned his B.S. in applied mathematics from Georgia Institute of Technology. As a National Science Foundation Graduate Research Fellow from 1990 to 1993, he received his Ph.D. in industrial and systems engineering from the University of Florida. His dissertation investigated production scheduling problems motivated by semiconductor manufacturing. He held a post-doctoral research position in the Institute for Systems Research from 1993 to 1995.

Dr. Herrmann’s research, service, and teaching activities have established him as a leader in the following areas: (1) developing novel mathematical models to improve public health preparedness, (2) describing and modeling engineering design decision-making processes, and (3) using risk-based techniques to improve path planning for autonomous systems. Through February 2023, he has published over 140 journal papers and refereed conference papers and fifteen book chapters, co-authored an engineering design textbook, edited two handbooks, and authored a textbook on engineering decision making and risk management. Dr. Herrmann and his colleagues have been awarded over $34 million in research contracts and grants, including research awards from NSF, Amazon, the Naval Air Warfare Center, the Air Force Research Laboratory, and the Army Research Laboratory. He has advised over 50 Ph.D. dissertations and M.S. theses. In 2003, Dr. Herrmann received the Society of Manufacturing Engineers Jiri Tlusty Outstanding Young Manufacturing Engineer Award; in 2013, he was named a Diplomate of the Society for Health Systems. In 2016, his textbook won the IIE/Joint Publishers Book of the Year award.


Bibliographic Information

  • Book Title: Metareasoning for Robots

  • Book Subtitle: Adapting in Dynamic and Uncertain Environments

  • Authors: Jeffrey W. Herrmann

  • Series Title: Synthesis Lectures on Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-32237-2

  • Publisher: Springer Cham

  • eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 12

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-32236-5Published: 13 May 2023

  • Softcover ISBN: 978-3-031-32239-6Published: 31 May 2024

  • eBook ISBN: 978-3-031-32237-2Published: 12 May 2023

  • Series ISSN: 1932-1228

  • Series E-ISSN: 1932-1686

  • Edition Number: 1

  • Number of Pages: XIII, 92

  • Number of Illustrations: 10 b/w illustrations, 18 illustrations in colour

  • Topics: Robotics and Automation, Artificial Intelligence, Artificial Intelligence

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