GS-PKNN: An Efficient and High-Fidelity Mobility Prediction Method for Unmanned Ground Vehicles

To avoid unmanned ground vehicles being ob- structed by deformed terrain in off-road, effective vehicle mobil- ity 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 (GS), 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 (PKNN), enabling accurate predictions of the global mobility map. Through rigorous simulation experiments, the proposed method (GS-PKNN) demonstrates its remarkable effectiveness.