Double Neural Networks Enhanced Global Mobility Prediction Model for Unmanned Ground Vehicles in Off-Road Environments

Currently, high-fidelity simulation techniques, such as discrete element methods, are widely employed for predicting the off-road mobility of unmanned ground vehicles, ensuring the successful completion of field missions. However, the traversal simulation process based on discrete element terrain models is highly time-consuming. To address this challenge, we propose a double neural networks enhanced global mobility prediction model. Firstly, acceleration of the vehicle-terrain contact simulation is achieved by developing an artificial neural network surrogate model. This model predicts stress and strain relationships based on a multiscale representative volume element terrain model, effectively replacing complex mechanical calculations. Secondly, the generation of the global mobility map is expedited by introducing a boundary data enhanced artificial neural network model. This model generates a training dataset by simulating Gaussian sampled terrain data and additional enhanced terrain data at the mobility classification boundary. The trained artificial neural network is subsequently utilized to predict the mobility map, thus circumventing the time-consuming process of traversing simulations. The simulation tests validate that our proposed method not only significantly reduces computation time but also ensures high prediction accuracy.