A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms Article Swipe
Thejus Pathmakumar
,
M. A. Viraj J. Muthugala
,
S. M. Bhagya P. Samarakoon
,
Braulio Félix Gómez
,
Mohan Rajesh Elara
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145317
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145317
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22145317
- https://www.mdpi.com/1424-8220/22/14/5317/pdf?version=1658198299
- OA Status
- gold
- Cited By
- 9
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285803834
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285803834Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22145317Digital Object Identifier
- Title
-
A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-16Full publication date if available
- Authors
-
Thejus Pathmakumar, M. A. Viraj J. Muthugala, S. M. Bhagya P. Samarakoon, Braulio Félix Gómez, Mohan Rajesh ElaraList of authors in order
- Landing page
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https://doi.org/10.3390/s22145317Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/22/14/5317/pdf?version=1658198299Direct link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/14/5317/pdf?version=1658198299Direct OA link when available
- Concepts
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Dirt, Robot, Audit, Path (computing), Computer science, Motion planning, Sample (material), Artificial intelligence, Engineering, Mechanical engineering, Business, Programming language, Chromatography, Chemistry, AccountingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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