Emergency driving scenarios typically demand longitudinal and lateral forces from tyres to slow the vehicle while performing an obstacle-avoidance manoeuvre. There is sufficient research on pure braking and pure cornering; however, in practice, braking and cornering are rarely performed independently. Active control systems such as Anti-lock Brakes (ABS) and Electronic Stability Control (ESC) react after detecting potential loss of yaw stability and are generally not effective for rollover prevention. A preventative approach is needed to manipulate load transfer and maximise tyre contact before tyre force limits are exceeded. This study investigates the use of a semi-active suspension system to improve rollover-prone, off-road vehicle performance during combined slip manoeuvres. By adjusting suspension characteristics, the system manipulates load transfer and improves overall traction limits, reducing instability and rollover risk. The methodology utilises a high-centre-of-mass off-road vehicle equipped with semi-active suspension alongside a high-fidelity multibody dynamics model for parametric studies. The model is validated through experimental open-loop manoeuvres using brake and steering robots. Findings indicate that the semi-active suspension, switched to hard front and soft rear settings, effectively improves combined slip performance and has the potential to mitigate rollover. Although currently tested on smooth surfaces, this research establishes a vital foundation for future off-road applications where terrain variability poses more severe stability challenges.
Tracked vehicles, valued for their superior mobility and stability, are widely used in forest operations. However, the complex topography of forested terrains—particularly sloped areas—limits the accuracy of traditional sinkage models that assume uniform ground pressure. To improve the prediction of sinkage and traction under varying slope angles and slip ratios, this study develops a traction prediction model for tracked vehicles operating on layered forest soils. The model captures the nonlinear coupling between track sinkage and slip ratio. A digital twin framework integrating the Discrete Element Method (DEM) and Multi-Body Dynamics (MBD) was employed. After verification using outdoor vehicle test data, the DEM-MBD simulation results were used as reference data to evaluate the proposed analytical model under different slip ratios and slope angles. The mean relative errors of the proposed model for track sinkage and net traction were 3.43% and 7.91%, respectively, lower than those of the conventional uniform-pressure and Bekker-based models, with errors of 26.87% and 14.83%. These findings demonstrate that the proposed model achieves improved prediction accuracy, offering theoretical and practical guidance for the structural design and performance optimization of tracked vehicles in complex forest environments.
Rapid characterization of soil surface is critical for military mobility planning, yet conventional methods require in-situ measurements that are time consuming, costly, and risky in unknown terrain. This study presents a near-real-time soil strength characterization framework that develops three models to predict soil texture, moisture, and Cone Index (CI) at the pixel level using only remotely sensed inputs. The developed workflow was validated at two sites within the Bundy Hill Off-Road Park, Jerome, Michigan, across two seasons (August 2024 and April 2025) using both drone and vehicle-mounted platforms at 50 cm resolution. Against 116 field measurements, the CI prediction achieved Spearman r = 0.81 and root mean squared error (RMSE) = 60.8 psi, outperforming the current baseline Soil Moisture Strength Prediction Model Version II (SMSP II model) (r = 0.69, RMSE = 78.2 psi) across all aggregate metrics. The improvement was most pronounced in the operationally critical marginal range (100–200 psi), where accurate prediction drives GO/NOGO mobility decisions. While CI maps provide an interpretable index of soil bearing capacity, the soil texture and moisture content predictions produced by this framework constitute the terrain inputs required by the Next Generation NATO Reference Mobility Model (NG-NRMM) for stochastic mobility mapping.
The interaction between tires and soil is widely recognized as a pivotal scientific topic in the field of off-road vehicle engineering, particularly with regard to the examination of tire traction performance when traversing granular wading-terrain. Granular wading-terrain herein refers to two types of granular conditions, which are a stratified terrain with water overlying dry sand, and a mixed water-soil terrain. This study aims to develop a tire-water-particle coupling numerical model based on the finite element method (FEM), smooth particle hydrodynamics (SPH), and discrete element method (DEM) to investigate the traction behavior of an FEM tire model on the SPH-DEM granular wading-terrain. Firstly, the effectiveness of the FEM-DEM, FEM-SPH, and DEM-SPH coupled methods is validated by comparing them with experimental results of the angle of repose, dam break, and water-entry problems. Then, a tire-water-particle FEM-DEM-SPH coupling numerical model is presented and employed to investigate the interaction mechanism between FEM tire models and granular terrain (DEM) with water (SPH), including drawbar pull, tire sinkage, and flow of soil and water particles. Also, the effects of water surface height, granular terrain moisture content, tire inflation pressure, tire load, and slip on the traction behavior of tires on wet terrains are investigated based on the proposed numerical model. The numerical results demonstrate that the FEM-DEM-SPH coupling method can accurately predict the tire-water-particle coupling problem, thereby providing a reliable tool for the design and optimization of tires and off-road vehicles in granular wading-terrain.
A physics-based terramechanics model is developed for predicting the maximum propulsion force of a single Archimedean screw traversing granular, low-cohesion soil. Building on classical grouser wheel-soil shear theory, the model applies the Mohr-Coulomb shear-failure criterion to the screw-soil interface to obtain a closed-form expression for the longitudinal and transverse forces generated as the screw displaces the soil. A geometric analysis relates the screw flight-soil contact area to the screw's rotation angle, sinkage depth, and flight geometry, providing the contact area required by the force model. The model is validated employing a custom test bench that measures the forces produced by additively manufactured screws of three distinct geometries moving at constant speed through Mojave Mars Simulant, a well-characterized granular medium. The predicted forces agree with experiment to within 10% for both the longitudinal and transverse components across all tested geometries. By linking soil shear properties and screw geometry to thrust in closed form, the model provides a practical basis for design, performance estimation, and future model-based control of screw-propelled off-road, planetary, and amphibious vehicles, for which conventional wheeled or tracked locomotion is poorly suited.
