End-to-End Current Consumption Estimation for a Driving System of a Mobile Robot Considering Geology

Mobile robots are often tasked with environmental surveys and disaster response operations. Accurately estimating the energy consumption of these robots during such tasks is essen-tial. Among the various components, the drive system consumes the most energy and exhibits the greatest fluctuations. Since these energy fluctuations stem from variations in current consumption, it is crucial to estimate the drive system’s current consumption with high accuracy. However, existing research faces challenges in accurately estimating current consumption, particularly when the ground geology changes or when internal states cannot be measured. Moreover, there is no clearly defined methodology for estimating the current consumption of a mobile robot’s drive system under unknown geological conditions or internal states. To address this gap, the present study aims to develop an end-to-end method for estimating the current consumption of a mobile robot’s drive system, taking ground geology into consideration. To achieve this, we propose a novel approach for collecting interaction data and generating a current consumption model. For data collection, we introduce a method that effectively captures the internal and external factors influencing the drive system’s current consumption, as well as their interactions. This is accomplished by treating the physical phenomena resulting from the interaction between the driving mechanism and the ground as vibrations. Additionally, we propose a method for generating a current consumption model using a neural network, accounting for measurement errors, outliers, noise, and global current fluctuations. The effectiveness of the proposed method is demonstrated through experiments conducted on three different ground types using a skid-steering mobile robot.

@article{Chikushi2025,
  title     = {End-to-End Current Consumption Estimation for a Driving System of a Mobile Robot Considering Geology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shota Chikushi and Yonghoon Ji and Hanwool Woo and Hitoshi Kono},
  doi       = {10.14569/IJACSA.2025.0160504},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160504}
}