Reliability-Based Global Path Planning under Uncertainty for Off-Road Autonomous Ground Vehicles

Off-road autonomous ground vehicles (AGVs) have gained increased attention in recent years due to their promising potential to be deployed in many areas to replace humans in harsh and/or boring working environments. Different from on-road conditions, off-road AGVs suffer from more complicated working conditions, in which vehicle-terrain interaction is much more complex, leading to unpredictable vehicle dynamic responses. Besides, uncertainty prevails in off-road terrain which is attributed to lacking precise environmental information. To solve the problems, this research will be carried out from three aspects. First, high-resolution terrain reconstruction employs low spatial resolution satellite images and soil maps. The spatial-dependent uncertainty of the reconstructed terrain (including both elevation and soil properties) induced by lacking precise environmental information is characterized. Second, the vehicle-terrain interaction model is constructed based on terramechan-ics, based on which a Bayesian machine learning model is used to learn the relationship between vehicle mobility in terms of speed-made-good and terrain properties (elevation and soil properties) and quantify the uncertainty of vehicle mobility. Third, a motion planning method integrating vehicle-terrain interaction with taking mobility uncertainty into account is developed, which will be verified by numerical simulations. This study could contribute part of the theoretical basis for motion planning of off-road AGVs under uncertain terrain conditions.