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Internal Rehearsals for a Reconfigurable Robot to Improve Area Coverage Performance

Published: 29 March 2024 Publication History

Abstract

Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot’s coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 3
June 2024
646 pages
EISSN:2157-6912
DOI:10.1145/3613609
  • Editor:
  • Huan Liu
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 March 2024
Online AM: 02 February 2024
Accepted: 24 January 2024
Revised: 14 December 2023
Received: 06 October 2022
Published in TIST Volume 15, Issue 3

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Author Tags

  1. Internal rehearsals
  2. area coverage
  3. context-aware reconfiguration
  4. reconfigurable robots

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  • Research-article

Funding Sources

  • National Robotics Programme under its National Robotics Programme (NRP) BAU, Ermine III: Deployable Reconfigurable Robots
  • A*STAR under its “RIE2025 IAF-PP Advanced ROS2-native Platform Technologies for Cross sectorial Robotics Adoption

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