Slip Estimation for Autonomous Tracked Vehicles via Machine Learning

Slip is a crucial parameter in the kinematic and dynamic models of tracked vehicles, which also exercises considerable influence over the track-ground interactions. The methods based on machine learning for slip estimation only employ proprioceptive sensor signals and obtain models by learning from numerous data, but they still have some limitations. This paper explores the applicability of such methods to autonomous tracked vehicles, which considers wider speed range, bends and difference between two tracks slip. First, a tremendous amount of data are collected with a tracked vehicle. Then road types identification is realized based on convolutional neural networks because the road types and slip are closely related. Lastly the piecewise regression method is employed to estimate slip. The performance of proposed approach is evaluated on test set. The results indicate that it can accurately identify the road(success rate, > 96%) and estimate the slip ratio(root mean square error, < 0.7%). The contrast experiment and analysis also show that classification before regression improves the accuracy and reduces the computational burden. Further, sparse Gaussian process regression is applied to return the distribution of slip, which can reflect the uncertainty of estimation.