Adaptive Terrain Traversability Prediction based on Multi-Source Transfer Gaussian Processes

Abstract

This study addresses the challenge of predicting the terrain traversability of off-road vehicles. When an off-road vehicle is operated on rough terrains or slopes of unconsolidated materials, it is crucial to accurately predict terrain traversability for efficient operations and to avoid critical mobility risks. However, the prediction of traversability is challenging, especially for the prediction of possibly risky terrains because for such terrains, the traverse data available is either limited or non-existent. To address this limitation, this study proposes an adaptive terrain traversability prediction method based on the multi-source transfer Gaussian process regression (MS-TGPR). The proposed method utilizes limited data available on low risk terrains of the target environment to enhance the prediction accuracy by leveraging past traverse experiences on multiple types of terrain surfaces. The effectiveness of the proposed method is demonstrated using a slip dataset of various terrain surfaces and geometries.