Advances in Plan-Based Control of Robotic Agents

Advances in Plan-Based Control of Robotic Agents

  • Michael Beetz
  • Leonidas Guibas
  • Joachim Herztberg
  • Malik Ghallab
  • Martha E. Pollack
Publisher:SpringerISBN 13: 9783540377245ISBN 10: 3540377247

Paperback & Hardcover deals ―

Amazon IndiaGOFlipkart GOSnapdealGOSapnaOnlineGOJain Book AgencyGOBooks Wagon₹5,375Book ChorGOCrosswordGODC BooksGO

e-book & Audiobook deals ―

Amazon India GOGoogle Play Books ₹43.44Audible GO

* Price may vary from time to time.

* GO = We're not able to fetch the price (please check manually visiting the website).

Know about the book -

Advances in Plan-Based Control of Robotic Agents is written by Michael Beetz and published by Springer. It's available with International Standard Book Number or ISBN identification 3540377247 (ISBN 10) and 9783540377245 (ISBN 13).

In recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva,and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA’s Deep Space program, for example, an - tonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scienti?c experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an o?ce - vironment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition. These autonomous robots have in common that they rely on plan-based c- trol in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is e?ective and has a high expected utility with respect to the robots’ c- rent goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based c- troller is able to manage and adapt the robot’s intended course of action — the plan — while executing it and can thereby better achieve complex and changing tasks.