Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784756565541126144 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9323497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT pathmakumarthejus anovelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT muthugalamavirajj anovelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT samarakoonsmbhagyap anovelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT gomezbrauliofelix anovelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT elaramohanrajesh anovelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT pathmakumarthejus novelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT muthugalamavirajj novelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT samarakoonsmbhagyap novelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT gomezbrauliofelix novelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms AT elaramohanrajesh novelpathplanningstrategyforacleaningauditrobotusinggeometricalfeaturesandswarmalgorithms |