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A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar do...

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Detalles Bibliográficos
Autores principales: Pathmakumar, Thejus, Muthugala, M. A. Viraj J., Samarakoon, S. M. Bhagya P., Gómez, Braulio Félix, Elara, Mohan Rajesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323497/
https://www.ncbi.nlm.nih.gov/pubmed/35890997
http://dx.doi.org/10.3390/s22145317
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author Pathmakumar, Thejus
Muthugala, M. A. Viraj J.
Samarakoon, S. M. Bhagya P.
Gómez, Braulio Félix
Elara, Mohan Rajesh
author_facet Pathmakumar, Thejus
Muthugala, M. A. Viraj J.
Samarakoon, S. M. Bhagya P.
Gómez, Braulio Félix
Elara, Mohan Rajesh
author_sort Pathmakumar, Thejus
collection PubMed
description Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.
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spelling pubmed-93234972022-07-27 A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms Pathmakumar, Thejus Muthugala, M. A. Viraj J. Samarakoon, S. M. Bhagya P. Gómez, Braulio Félix Elara, Mohan Rajesh Sensors (Basel) Article Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage. MDPI 2022-07-16 /pmc/articles/PMC9323497/ /pubmed/35890997 http://dx.doi.org/10.3390/s22145317 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pathmakumar, Thejus
Muthugala, M. A. Viraj J.
Samarakoon, S. M. Bhagya P.
Gómez, Braulio Félix
Elara, Mohan Rajesh
A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title_full A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title_fullStr A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title_full_unstemmed A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title_short A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
title_sort novel path planning strategy for a cleaning audit robot using geometrical features and swarm algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323497/
https://www.ncbi.nlm.nih.gov/pubmed/35890997
http://dx.doi.org/10.3390/s22145317
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