Energy-Based Surface Classification for Mobile Robots in Known and Unexplored Terrains
| Parent link: | Robotics.— .— Basel: MDPI AG Vol. 14, iss. 9.— 2025.— Article number 130, 17 p. |
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| Riassunto: | Title screen Mobile robot navigation in diverse environments is challenging due to varying terrain properties. Underlying surface classification improves robot control and navigation in such conditions. This paper presents an adaptive surface classification system using proprioceptive energy consumption data. We introduce an energy coefficient, calculated from motor current and velocity, to quantify motion effort. This coefficient’s dependency on motion direction is modeled for known surface types using discrete cosine transform. A probabilistic classifier, enhanced with memory, compares real-time coefficient values against these models to identify known surfaces. A neural network-based detector identifies encounters with previously unknown terrains by recognizing significant deviations from known models. Upon detection, a least squares method identifies the new surface’s model parameters using data gathered from specific motion directions. Experimental results validate the approach, demonstrating high classification accuracy for known surfaces (91%) and robust detection (96.2%) and identification (MAPE < 3%) of unknown surfaces Текстовый файл |
| Lingua: | inglese |
| Pubblicazione: |
2025
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| Accesso online: | https://doi.org/10.3390/robotics14090130 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=681980 |
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| 200 | 1 | |a Energy-Based Surface Classification for Mobile Robots in Known and Unexplored Terrains |f Alexander Belyaev, Oleg Kushnarev | |
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| 330 | |a Mobile robot navigation in diverse environments is challenging due to varying terrain properties. Underlying surface classification improves robot control and navigation in such conditions. This paper presents an adaptive surface classification system using proprioceptive energy consumption data. We introduce an energy coefficient, calculated from motor current and velocity, to quantify motion effort. This coefficient’s dependency on motion direction is modeled for known surface types using discrete cosine transform. A probabilistic classifier, enhanced with memory, compares real-time coefficient values against these models to identify known surfaces. A neural network-based detector identifies encounters with previously unknown terrains by recognizing significant deviations from known models. Upon detection, a least squares method identifies the new surface’s model parameters using data gathered from specific motion directions. Experimental results validate the approach, demonstrating high classification accuracy for known surfaces (91%) and robust detection (96.2%) and identification (MAPE < 3%) of unknown surfaces | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Robotics |c Basel |n MDPI AG | |
| 463 | 1 | |t Vol. 14, iss. 9 |v Article number 130, 17 p. |d 2025 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a AI-based methods | |
| 610 | 1 | |a energy consumption | |
| 610 | 1 | |a mobile robots | |
| 610 | 1 | |a proprioception | |
| 610 | 1 | |a surface classification | |
| 700 | 1 | |a Belyaev |b A. S. |c Specialist in the field of informatics and computer technology |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1994- |g Aleksandr Sergeevich |9 20707 | |
| 701 | 1 | |a Kushnarev |b O. Yu. |g Oleg Yurjevich | |
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