Slip estimation and classification using in-wheel sensor for mobile robot in sandy terrain

Sandy terrain often traps wheeled vehicle or mobile robot with immobilizing wheel stuck. The wheel stuck phenomenon is highly related to wheel slippage and soil failure. Therefore, wheel slip detection and estimation is particularly important for avoiding the wheel stuck phenomenon. This paper proposes a method that can estimate and classify a magnitude of wheel slip using an in-wheel sensor system. The in-wheel sensor captures wheel-terrain interaction characteristics such as contact angles and normal force around the wheel. The proposed method basically estimates a wheel slip by comparing the measured data from the in-wheel sensor with a look-up table generated by a machine learning algorithm. Training data for the machine learning is a variety of experimental data set given from the in-wheel sensor. The look-up table developed in this work distinguishes the magnitude of wheel slippage into three categories: non-stuck wheel, quasi-stuck wheel, and stuck wheel. Experimental demonstration of the proposed method achieves the slip estimation with an accuracy of 90 % or more. Moreover, it is found that tracking the interaction characteristics in a spatiotemporal manner can predict an immobilizing wheel slip or even wheel stuck, resulting in a decrease of mobility hazard.