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JRM Vol.29 No.5 pp. 902-910
doi: 10.20965/jrm.2017.p0902
(2017)

Paper:

Wheel Slip Classification Method for Mobile Robot in Sandy Terrain Using In-Wheel Sensor

Takuya Omura and Genya Ishigami

Keio University
3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japan

Received:
March 20, 2017
Accepted:
June 16, 2017
Published:
October 20, 2017
Keywords:
wheel slip classification, wheel-soil interaction, in-wheel sensor, support vector machine
Abstract
Wheel Slip Classification Method for Mobile Robot in Sandy Terrain Using In-Wheel Sensor

Test bed rover with in-wheel sensor

This paper proposes a method that can estimate and classify the magnitude of wheel slippage for a mobile robot in sandy terrains. The proposed method exploits a sensor suite, called an in-wheel sensor, which measures the normal force and contact angle at the wheel-sand interaction boundary. An experimental test using the in-wheel sensor reveals that the maximum normal force and exit angle of the wheel explicitly vary with the magnitude of the wheel slippage. These characteristics are then fed into a machine learning algorithm, which classifies the wheel slippage into three categories: non-stuck wheel, quasi-stuck wheel, and stuck wheel. The usefulness of the proposed method for slip classification is experimentally evaluated using a four-wheel-drive test bed rover.

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Last updated on Dec. 12, 2017