Efficient and High-Fidelity Mobility Prediction for Unmanned Ground Vehicles Based on Gaussian Sampled Terrain and Enhanced Neural Network

Abstract

To avoid unmanned ground vehicles being obstructed by deformed terrain in off-road, effective vehicle mobility analysis is required. However, the computational complexity of existing mobility analysis methods, such as discrete element analysis, poses significant challenges when applied to large-scale terrains. To address this problem, we propose an efficient and high-fidelity vehicle mobiliy prediction method for a large-scale terrain. Initially, precise terrain models are constructed employing Gaussian sampling, thereby serving as optimal inputs for the mobility simulation. Subsequently, we introduce a co-simulation method based on a multi-body dynamics model and discrete element analysis to obtain high-fidelity vehicle mobility data on sampled terrains. Following that, the mobility data is utilized to train a PSO-kriging neural network, enabling accurate predictions of the global mobility map. Through rigorous simulation experiments, the proposed method demonstrates its remarkable effectiveness.