Worley, R. orcid.org/0000-0002-3607-2650, Yu, Y. and Anderson, S. orcid.org/0000-0002-7452-5681 (2020) Acoustic echo-localization for pipe inspection robots. In: 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 14-16 Sep 2020, Karlsruhe, Germany. Institute of Electrical and Electronics Engineers (IEEE) , pp. 160-165.
Abstract
Robot localization in water and wastewater pipes is essential for path planning and for localization of faults, but the environment makes it challenging. Conventional localization suffers in pipes due to the lack of features and due to accumulating uncertainty caused by the limited perspective of typical sensors. This paper presents the implementation of an acoustic echo based localization method for the pipe environment, using a loudspeaker and microphone positioned on the robot. Echoes are used to detect distant features in the pipe and make direct measurements of the robot's position which do not suffer from accumulated error. Novel estimation of echo class is used to refine the acoustic measurements before they are incorporated into the localization. Finally, the paper presents an investigation into the effectiveness of the method and the robustness of the method to errors in the acoustic measurements.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Engineering; Information and Computing Sciences; Control Engineering, Mechatronics and Robotics; Artificial Intelligence; Data Management and Data Science |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/S016813/1 Engineering and Physical Sciences Research Council EP/N010124/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jul 2024 08:38 |
Last Modified: | 08 Jul 2024 08:43 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/mfi49285.2020.9235225 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214497 |