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The surface of the near-Earth planets, such as Mars, is covered with fine-grained regolith and other objects, such as rocks, craters, and sand dunes. In addition, the gravity of Mars is weaker than that of Earth, making rover operations on Mars more challengeable, such as higher slip, wheel sinkage, and lower tractive force, etc. In this paper, we use the AlexNet to establish a model for identifying the types of the rover rut on Mars. The model is used to predict trafficability based on the images of the rover rut which comprise the wheel-soil interaction. The types of the rut are evaluated by the level of slip ratio. According to the value of the slip ratio, the rut is classified as three types: small, middle, and large range. The dataset used in this work includes 203 samples, which was split into the training and the validation sets consisting of 143 and 60 data samples, respectively. The experimental results show that the AlexNet based on the transfer learning can accurately predicts the type of the rut. The training and validation accuracies obtained by the proposed method are 94.44% and 91.46%, respectively. The proposed technique can be used to improve the ability of environment perception, risk assessment, and trafficability estimation. This work provides the knowledge and strategy in developing future rovers for deep space planetary explorations.
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