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Can a Skywalker Localize the Midpoint of a Rope?

Published: 18 July 2021 Publication History

Abstract

This article poses a question about a simple localization problem. The question is if an oblivious walker on a line segment can localize the midpoint of the line segment in a finite number of steps observing the direction (i.e., Left or Right) and the distance to the nearest end point. This problem arises from self-stabilizing location problems by autonomous mobile robots with limited visibility, which is an abstract model attracting a wide interest in distributed computing. Contrary to appearances, it is far from trivial whether this simple problem is solvable, and it is not settled yet. This article is concerned with three variants of the problem with a minimal relaxation and presents self-stabilizing algorithms for them. We also show an easy impossibility theorem for bilaterally symmetric algorithms.

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Published In

cover image ACM Transactions on Computation Theory
ACM Transactions on Computation Theory  Volume 13, Issue 3
September 2021
146 pages
ISSN:1942-3454
EISSN:1942-3462
DOI:10.1145/3476826
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2021
Accepted: 01 March 2021
Revised: 01 September 2020
Received: 01 March 2019
Published in TOCT Volume 13, Issue 3

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Author Tags

  1. Self-stabilization
  2. autonomous mobile robot
  3. choice axiom
  4. computable real
  5. small space algorithm

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