# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
###### Abstract  
Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China's Mars rover, Zhurong, using Gaussian process regression (GPR). The model was able to predict not only the average value of the longitudinal (slip_x) and lateral slip (slip_y), but also the maximum possible value that slip_x and slip_y could reach. The training data were collected on two simulated soils, TYII-2 and JLU Mars-2, and the GA-BP algorithm was applied as a comparison. The analysis results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were closer to the JLU Mars-2 simulated soil. The proposed GPR model had high estimation accuracy and estimation potential in slip value, and a 95% confidence interval that the rover could reach during motion. This work was part of a research effort aimed at ensuring the safety of Zhurong. The slip value may be used in subsequent path tracking research, and the slip confidence interval will be able to help guide path planning. M +
Footnote †: journal: Applied sciences  
doi: 10.3390/  
(1)  
Exteroceptive-based approach  
Exteroceptive sensors require input from the environment and the rover's surroundings. The most widely used exteroceptive sensors are cameras. Cameras obtain image information for slip estimation, which can be subdivided into slip estimation based on natural environment images and slip estimation based on wheel-ground images. Slip estimation based on natural environment images is usually realized by Visual Odometer (VO). The rover continuously obtains image data of the surrounding environment using cameras and calculates its own pose change according to the overlapping information of adjacent images. It uses these to obtain a motion estimation and then combines that with wheel speed information to calculate the slip rate [10; 11]. VO is widely used in planetary exploration, and has been used with the Mars rovers Spirit, Opportunity and Curiosity, as well as the lunar rovers Tutu and Yutu-2. This method has high slip estimation accuracy, but it incurs high computational costs, and is limited for use in sparse-featureed areas and shadowed areas [12; 13]. The slip estimation based on wheel-ground images judges the slip using image information, such as ruts and wheels. [PERSON] et al. [14] proposed to use monocular vision to judge the wheel slip rate under ideal conditions without side deflection, based on the ruts' spacing. [PERSON] et al. [15; 16] established the diffuse analysis model of trace imprint by analyzing the formation mechanism of the wheel imprint, and realized slip estimation based on the time domain features of the wheel imprint image. [PERSON] et al. [17] also proposed a high-brightness weak boundary rut extraction method to estimate wheel linear velocity and combined it with wheel angular velocity (estimated by tracking the wheel marking points) to estimate the wheel slip. Compared with the natural environment-based slip estimation method, the slip estimation method based on wheel-ground images has better robustness; it overcomes the acquisition of surface feature points and the dependence on lighting conditions. Additionally, the computational cost is significantly reduced. However, related research focused on wheel slip rate estimations has been performed under ideal conditions, e.g., no side deflection. These conditions do not conform to the complex situation of orbital operation of a planetary rover. (2)  
Proprioceptive-based approach  
Proprioceptive sensors only require signals from the rover, without depending on surrounding environment information. The lunar rovers, Lunokhod-1 and Lunokhod-2, added a non-driving wheel that could roll up and down freely at the rear of the rover. The actual forward distance of the lunar rover was measured by the number of revolutions of this wheel.