AI-Enabled Condition Monitoring Framework for Autonomous Pavement-Sweeping Robots Article Swipe
Sathian Pookkuttath
,
A. Zin
,
Akhil Jayadeep
,
M. A. Viraj J. Muthugala
,
Mohan Rajesh Elara
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13142306
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13142306
The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies.
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https://doi.org/10.3390/math13142306Digital Object Identifier
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AI-Enabled Condition Monitoring Framework for Autonomous Pavement-Sweeping RobotsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-18Full publication date if available
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Sathian Pookkuttath, A. Zin, Akhil Jayadeep, M. A. Viraj J. Muthugala, Mohan Rajesh ElaraList of authors in order
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https://doi.org/10.3390/math13142306Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/math13142306Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.gradients, | 86 |
| abstract_inverted_index.heavy-duty | 4 |
| abstract_inverted_index.introduces | 49 |
| abstract_inverted_index.moderately | 99, 102 |
| abstract_inverted_index.monitoring | 53, 224 |
| abstract_inverted_index.prediction | 125, 265 |
| abstract_inverted_index.shortages. | 18 |
| abstract_inverted_index.unevenness | 33, 60, 83 |
| abstract_inverted_index.workspace. | 241 |
| abstract_inverted_index.appropriate | 286 |
| abstract_inverted_index.conditions. | 72 |
| abstract_inverted_index.consumption | 79, 206 |
| abstract_inverted_index.identified: | 97 |
| abstract_inverted_index.large-scale | 138 |
| abstract_inverted_index.maintenance | 41, 280, 293 |
| abstract_inverted_index.measurement | 259 |
| abstract_inverted_index.performance | 243 |
| abstract_inverted_index.prediction. | 187 |
| abstract_inverted_index.statistical | 258 |
| abstract_inverted_index.strategies. | 294 |
| abstract_inverted_index.acceleration | 198 |
| abstract_inverted_index.application. | 134 |
| abstract_inverted_index.demonstrates | 273 |
| abstract_inverted_index.environments | 28 |
| abstract_inverted_index.experiments. | 151 |
| abstract_inverted_index.large-scale, | 3 |
| abstract_inverted_index.convolutional | 113 |
| abstract_inverted_index.heterogeneous | 162 |
| abstract_inverted_index.interoceptive | 169 |
| abstract_inverted_index.respectively. | 271 |
| abstract_inverted_index.distinguishing | 67 |
| abstract_inverted_index.representative | 159 |
| abstract_inverted_index.one-dimensional | 112 |
| abstract_inverted_index.pavement-sweeping | 6, 140 |
| abstract_inverted_index.simple-structured | 111 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5064833895 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I152815399 |
| citation_normalized_percentile |