A wheel simulator for agricultural machinery on rough UAV-reconstructed field terrain meshes

Unmanned aerial vehicle (UAV)-based photogrammetry provides a practical way to reconstruct farmland terrain for digital agricultural simulation, but the resulting triangular meshes preserve high-frequency microrelief and local depressions that differ fundamentally from the smooth and structured surfaces assumed in many generic vehicle simulators. When single-point wheel–ground contact is applied directly to such meshes, unstable support normals and normal forces can induce nonphysical artifacts, including geometry-induced stuck states and subsequent burst events, during low-speed operation. To address this problem, this study proposes a wheel simulation framework for rough farmland meshes based on principal component analysis (PCA)-based patch estimation (PPE). PPE aggregates multiple local contact candidates, estimates an effective wheel-patch support plane and support normal through PCA, and determines a representative contact position using soft gating. The estimated support is then coupled with an effective rigid-mesh normal-compression relation, a simplified Pacejka wheel force mapping, and explicit wheel rotational dynamics. The framework was evaluated on UAV-reconstructed farmland meshes through static support, multi-speed straight-line, and constant-steering turning tests, together with terrain-response, cross-region robustness, and computational performance analyses. Compared with single-point detection and an engine-provided wheel model, PPE consistently produced more stable support normals and normal forces, reproduced measured straight-line behavior more closely across the tested low-speed range, suppressed abnormal stuck-burst events, and preserved a steering behavior trend closer to that of a real tractor. The method also remained stable under controlled increases in mesh roughness and across real UAV-reconstructed regions with different spatial roughness patterns. Compared with flat terrain, the UAV-reconstructed terrain produced a speed-response spectrum closer to the measured tractor response and stronger simulator-level coherence between the contact-derived terrain-response indicator and vehicle speed fluctuation. Execution time profiling further confirmed that PPE satisfied the 50 Hz real-time simulation budget on the tested hardware. These results show that stable virtual tractor operation can be achieved directly on rough reconstructed farmland meshes while preserving terrain-induced response patterns relevant to real vehicle behavior, thereby supporting digital tractor simulation based on surveyed field geometry and future agricultural digital twin applications.