Slippage Estimation via Few-Shot Learning Based on Wheel-Ruts Images for Wheeled Robots on Loose Soil

When a wheeled mobile robot (WMR) runs on loose soil (such as the planetary rover on surface of the planet), its wheels generally slip or skip. Since the slippage of the wheel directly affects the motion control and safety, it becomes urgent to effectively estimate the slippage. In this paper, an intuitive slippage estimation method is proposed based on wheel-ruts images, which aims to reduce the number of extra sensors and take advantages of the visual information effectively. Since the image samples sometimes are difficult to collect, a few-shot learning method is employed using distribution propagation graph network with dilated causal convolution layer (DCC-DPGN). The dilated causal convolution (DCC) layer is adopted in ResNet block to expand the receptive field and obtain the sequence information of wheel-ruts, which makes the model training more efficient. The proposed model is verified in the test set of images collected in real scene, which shows the potential of the proposed algorithm in slippage estimation.