Energy-Based Surface Classification for Mobile Robots in Known and Unexplored Terrains

Energy-Based Surface Classification for Mobile Robots in Known and Unexplored Terrains

Bibliografiska uppgifter
Parent link:Robotics.— .— Basel: MDPI AG
Vol. 14, iss. 9.— 2025.— Article number 130, 17 p.
Huvudupphovsman: Belyaev A. S. Aleksandr Sergeevich
Övriga upphovsmän: Kushnarev O. Yu. Oleg Yurjevich
Sammanfattning: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
Språk:engelska
Publicerad: 2025
Ämnen:
электронный ресурс
труды учёных ТПУ
AI-based methods
energy consumption
mobile robots
proprioception
surface classification
Länkar:https://doi.org/10.3390/robotics14090130
Materialtyp: Elektronisk Bokavsnitt
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=681980

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