# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
###### Abstract
This was combined with the number of revolutions to estimate the rover slip [18]. The Mars rover Curiosity determined whether it was in danger of slipping by comparing its current average to a safety threshold [19]. These methods are not suitable for uneven surfaces. With the rise of data analytics and machine learning, proprioceptive-based data-driven approaches have been applied to rover slip estimation. In these methods, the selected signal data and the corresponding slip observations are used as input and output, respectively, and the model is trained through machine learning algorithms to obtain slip  
Figure 1: Most common slip estimation approaches for rovers. estimation. [PERSON] et al. [5; 19] used linear acceleration, vertical acceleration, and pitch rate, measured by Inertial Measurement Unite (IMU), as input features, and realized the slip level classification through various machine learning algorithms, such as Artificial Neural Network (ANN), Support Vector Machines (SVM), Self-Organizing Mapping (SOM), and so on. The Lunar All-Terrain Utility Vehicle (LATUV) rover was used to validate the models, and a concrete-making factory and an abandoned mine were chosen to be test sites. [PERSON] et al. [20] took driving torque, longitudinal force, and friction coefficient as inputs, constructed a terrain classifier through SVM, and associated terrain types with slip level labels. Sand types corresponded to high slip and rock types corresponded to low slip. The research was tested using the rover SherpaTT, provided by German Research Center for Artificial Intelligence, and the test site was an outdoor area in soft sand dunes in Morocco. The proprioceptive-based data-driven approach is becoming a common trend in slip estimation research, as it can adapt to complex environment and overcome the dependence on images of the surrounding environment. However, few studies have taken into account the differences in gravitational acceleration and soil conditions between Earth and Mars or the Moon. The datasets used to train and validate these models were almost all obtained on completely natural Earth terrain, which cannot guarantee applicability of the trained models on Mars or the Moon. China's first Mars rover Zhurong successfully landed on the southern Utopia Planitia of Mars at 109.925\({}^{\circ}\) E, 25.066\({}^{\circ}\) N on 15 May 2021 [21]. Figure 2 shows the routing path of Zhurong during the three-month-long designed life and the typical pictures returned by Zhurong. After completing its primary exploration tasks, Zhurong continued to carry out an extended expedition toward an ancient coastal area of Utopia Planitia. Inevitably, it would have to pass through complex terrains with slip-prone areas in the exploration process. The primary aim of our research was to establish a slip estimation model for the Mars rover Zhurong. In our previous work [22; 23], we used the Back Propagation neural network (BP) and optimized BP based on a genetic algorithm (GA-BP) to train models, and GA-BP models were proven to have good slip estimation performance by model evaluation and validation. However, GA-BP models did not take uncertainty into consideration. As slip estimation models are expected to provide the basis for slip-based path planning and path tracking, obtaining uncertainty in slip estimation has significance for subsequent research. This paper was a continuation of previous work; we established models able to estimate slip values and confidence intervals, and further discussed models based on different soil types. All data were collected in a test site equipped with a low-gravity simulation device and simulated soil, such that we were able to simulate the conditions experienced by [PERSON] on Mars as closely as possible. The rest of this paper has been organized as follows: in Section 2, we illustrated the research framework of this paper. In Section 3, we introduced the data collection process of ground tests. In Section 4, we introduced the training methods.