Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

Nicolò Botteghi*, Luuk Grefte, Mannes Poel, Beril Sirmacek, Christoph Brune, Edwin Dertien, Stefano Stramigioli

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
70 Downloads (Pure)

Abstract

Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. We introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control. A video of our experiments can be found at: https://youtu.be/uyjSHulpGoI.

Original languageEnglish
Title of host publicationRobot Intelligence Technology and Applications 6 - Results from the 9th International Conference on Robot Intelligence Technology and Applications
EditorsJinwhan Kim, Brendan Englot, Hae-Won Park, Han-Lim Choi, Hyun Myung, Junmo Kim, Jong-Hwan Kim
PublisherSpringer
Pages259-271
Number of pages13
ISBN (Print)9783030976712
DOIs
Publication statusPublished - 2022
Event9th International Conference on Robot Intelligence Technology and Applications, RiTA 2021 - Daejeon, Korea, Republic of
Duration: 16 Dec 202117 Dec 2021
Conference number: 9

Publication series

NameLecture Notes in Networks and Systems
Volume429 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference9th International Conference on Robot Intelligence Technology and Applications, RiTA 2021
Abbreviated titleRiTA 2021
Country/TerritoryKorea, Republic of
CityDaejeon
Period16/12/2117/12/21

Keywords

  • In-pipe inspection robotics
  • Reinforcement learning

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