Vehicle mobility on highly organic soils

Current political climates have generated a renewed interest in the northern regions of the world. These areas are known to have soft marshy peat, highly organic soils, and harsh winter climates. Current capabilities for vehicle mobility modeling on this terrain is limited and existing studies do not include contemporary military vehicles. This work presents mobility experiments of modern military vehicles at multiple field sites containing peat or highly organic soils that can be used to improve mobility modeling on these soils. Field experiments are being conducted during multiple seasons, including winter, spring, and summer. The vehicle traction, motion resistance, and hard surface rolling resistance of an instrumented High Mobility Multipurpose Wheeled Vehicle (HMMWV) and a Small Unit Support Vehicle (SUSV) were examined. The first is a common multi-purpose vehicle and the second is a vehicle designed to operate in these types of environments. This data set will provide the basis for model development and validation for vehicle mobility in highly organic soils.     

Influence of turning radius on wheeled military vehicle induced rut formation

A rut is a depression or groove formed into the ground by the travel of wheels and tracks. Ruts can cause severe influences on soil and vegetation, and reduce vehicle mobility. In this paper, rut depth and rut width were used as the main indicators to quantify a rut. A new indicator, rut index, was proposed, combining rut depth and rut width. A Light Armored Vehicle (LAV) and a High Mobility Multi-purpose Wheeled Vehicle (HMMWV) were used for testing the influence of turning radius on rut depth, rut width and rut index. The LAV and the HMMWV were operated in spiral patterns at different speeds. Differential GPS data for the vehicles were collected every second during the spiral. Rut measurements were manually taken every 4-7 m along each of the spiral tracks. The results of field tests indicate that rut depth, rut width and rut index increase with the decrease of turning radius, especially when turning radius is less than 20 m. Velocity influences rut formation for the LAV but not HMMWV.     

Overview of cold regions mobility modeling at CRREL

Over the last several decades, the Cold Regions Research and Engineering Laboratory (CRREL) has extensively tested and analyzed issues related to vehicle performance in winter. Using this knowledge and the experimental database, models were developed to capture the important elements for cold regions mobility performance. These models span a range of resolutions and fidelities and include three-dimensional finite element models of tire–terrain interaction, vehicle dynamics models of vehicles on winter surfaces, semi-empirical cold regions algorithms for winter performance within the NATO reference mobility model (NRMM), all-season vehicle performance in force-on-force war-gaming simulations, and vehicle–surface interaction for real-time vehicle simulators. Each of these types of models is presented along with examples of their application.     

Exploring cold regions autonomous operations

The US Army must update its vehicle fleet to be better equipped for potential future military conflicts in northern climates (US Army, 2017). This process involves considering manned, optionally manned, and unmanned vehicles as viable options in the future. Optionally manned and unmanned vehicles in the armed forces have substantial benefits because they can operate without direct driver input or are able to perform missions deemed too dangerous for troops. Optionally manned vehicles allow the driver to shift some, or all, focus away from the task of driving the vehicle. In some cases, these autonomous vehicles may perform better than a human driver by rapidly sensing and reacting to terrain changes. Onboard sensing and decision making are equally applicable to both fully autonomous and teleoperated vehicles. This work will focus on the terrain sensing, waypoint navigation, and teleoperation potential of an optionally manned or unmanned vehicle. Results from a vehicle demonstration on two different terrain conditions will provide the basis for additional terrain sensing and autonomous vehicle development work in the coming year.     

Agile tire slippage dynamics for radical enhancement of vehicle mobility

There is a need to radically increase mobility of terrain vehicles through new modalities of vehicle locomotion, i.e., by establishing a new technological paradigm in vehicle dynamics and mobility. The new paradigm greatly applies to military vehicles for the radical improvement of tactical and operational mobility. This article presents a new technological paradigm of agile tire slippage dynamics that is studied as an extremely fast and exact response of the tire–soil couple to (i) the tire dynamic loading, (ii) transient changes of gripping and rolling resistance conditions on uniform stochastic terrains and (iii) rapid transient changes from one uniform terrain to a different uniform terrain. Tire longitudinal relaxation lengths are analyzed to characterize the longitudinal relaxation time constants. A set of agile characteristics is also considered to analyze agile tire slippage dynamics within a time interval that is close to the tire longitudinal relaxation time constants. The presented paradigm of agile tire slippage dynamics lays out a foundation to radically enhance vehicle terrain mobility by controlling the tire slippage in its transient phases to prevent the immobilization of a vehicle. Control development basis and requirements for implementing an agile tire slippage control are also analyzed and considered.     

Discrete element modelling of large soil deformations under heavy vehicles

This paper addresses the challenges of creating realistic models of soil for simulations of heavy vehicles on weak terrain. We modelled dense soils using the discrete element method with variable parameters for surface friction, normal cohesion, and rolling resistance. To find out what type of soils can be represented, we measured the internal friction and bulk cohesion of over 100 different virtual samples. To test the model, we simulated rut formation from a heavy vehicle with different loads and soil strengths. We conclude that the relevant space of dense frictional and frictional-cohesive soils can be represented and that the model is applicable for simulation of large deformations induced by heavy vehicles on weak terrain.     

Vehicle movement patterns and vegetative impacts during military training exercises

The operation of off-road vehicles during military training exercises can affect the environmental conditions of training lands by removing or disturbing vegetation. The use of global positioning systems (GPS)-based vehicle tracking systems can help to characterize the movement of vehicles during training exercises for the purpose of quantifying vegetative impacts. The combination of GPS positions of vehicles in the field during a training exercise, and geographic information system (GIS) maps of the training installation can provide information about vehicle-specific vegetation impacts of a training exercise, as related to vehicle locations, turning radius and velocity. Such relationships can be used to estimate off-road vegetation impacts. Twenty GPS-based vehicle tracking systems were installed on vehicles of the US Army 3rd Brigade 1/14 Cavalry to evaluate vegetation impacts during a 10 day reconnaissance training exercise at Yakima Training Center in Yakima, WA. The vehicle tracking systems were programmed to record the position of the vehicles every second. The resulting vehicle tracking data were analyzed for quantity of travel per day of the training activity, quantity of travel on and off roads, off-road vehicle dynamic properties turning radius and velocity, and off-road vegetation removed. The vehicles were in motion an average of 8.4% (approximately 2 h per day) of the training exercise time. The average distance traveled per day on roads was 33.5 km, and the average distance traveled per day off-roads was 7.7 km. On average, the vehicles spent 16% of their off-road traveling time at turning radii less than 20 m. Vegetation impacts were compared for different missions. The zone reconnaissance mission produced the highest vegetation impact per distance traveled.     

Next-generation NATO reference mobility model complex terramechanics – Part 1: Definition and literature review

The US army along with NATO member and partner nations’ militaries need an accurate software tool for predicting ground vehicle mobility (such as speed-made-good and fuel-consumption) on world-wide terrains where military vehicles may be required to operate. Currently, the NATO Reference Mobility Model (NRMM) is the only NATO recognized tool for assessing ground vehicle mobility. NRMM was developed from the 1960s to the 1980s and relies on steady-state empirical formulas which may not be accurate for new military ground vehicles. A NATO research task group (RTG-248) was established from 2016 to 2018 to develop the NG-NRMM (next-generation NRMM) software tool requirements and an NG-NRMM prototype which uses high-fidelity “simple” or “complex” terramechanics models for the terrain/soil along with modern 3D multibody dynamics software tools for modeling the vehicle. NG-NRMM Complex Terramechanics (CT) models are those that utilize full 3D soil models capable of predicting the 3D soil reaction forces on the vehicle surfaces (including tires, tracks, legs, and under body) and the 3D flow and deformation of the soil including both elastic and plastic deformation under any 3D loading condition. In Part 1 of this paper, an overview of the full spectrum of terramechanics models from the highest fidelity to the lowest fidelity is presented along with a literature review of CT ground vehicle mobility models.     

Influence of soil and vehicle parameters on soil rut formation

Soil and vehicle parameters have significant effects on soil rut formation. A randomized design was used to investigate the effects of five treatments: soil texture, soil moisture, vehicle type, turning radius and velocity, on rut depth, rut width and rut index, which measure the degree of soil disturbance. This vehicle rutting study was conducted on four off-road military vehicles under two soil moisture conditions and two soil texture conditions at Fort Riley, Kansas. A GPS-based vehicle tracking system was used to track the vehicle dynamics, and rut measurements were taken manually. SAS 9.1 was used to investigate the effects of soil and vehicle parameters on rut formation. Results show that all the vehicle parameters (vehicle type, weight, velocity and turning radius) and soil parameters (soil texture and moisture) are statistically significant to affect rut formation.     

Clarification of vehicle cone index with reference to mean maximum pressure

The US Army developed Vehicle Cone Index (VCI) as a metric for directly quantifying the ability of vehicles to traverse soft-soil terrain. In order to ensure minimum soft-soil performance capabilities for their new military vehicles, the US Army has used VCI for many years as a performance specification. The United Kingdom’s Ministry of Defence (UK MOD) has used the Mean Maximum Pressure (MMP) parameter for many years as a performance specification. It has been demonstrated that the MMP parameter relates to soft-soil performance capabilities, and hence, the UK MOD has ensured minimum performance capabilities for their new military vehicles by using MMP specifications. Both the VCI and MMP specification approaches have served their users well, but fundamental differences in the two specification approaches have produced some misunderstandings concerning what VCI really is and how it relates to MMP. This article clarifies that VCI is a performance metric, not a set of predictive equations, explains how VCI is measured, and compares different methods of predicting VCI for one-pass performance (i.e., VCI₁) of wheeled vehicles in fat clay soils. It is further clarified that MMP should not be compared with VCI but instead with Mobility Index (MI), which is the principal parameter used by the US Army for predicting VCI. Relationships are presented for using MMP to predict VCI₁ for wheeled vehicles in clay, and the resulting relationships allow comparison between MMP and MI in terms of their ability to predict VCI. Seventy-nine VCI₁ performance measurements were used for the comparison, and they demonstrate that MI describes the historical performance data somewhat better than MMP.     

The Next Generation NATO Reference mobility model development

The NATO Reference Mobility Model (NRMM) is a simulation tool aimed at predicting the capability of a vehicle to move over specified terrain conditions. NRMM was developed and validated by the U.S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC) and Engineer Research and Development Center (ERDC) in the 1960s and ‘70s, and has been revised and updated through the years, resulting in the most recent version, NRMM v2.8.2b. It was originally used to facilitate comparison between vehicle design candidates by assessing the mobility of existing vehicles under specific terrain scenarios, but has subsequently and most recently found expanded use in support of complex decision analyses associated with vehicle acquisition and operational planning support. This paper summarizes recent efforts initiated under a NATO Exploratory Team (ET) and its follow-on Research Task Group (RTG) to upgrade this key modeling and simulation tool and the planned path forward toward implementing the recommendations of that team.     

On the impact of cargo weight, vehicle parameters, and terrain characteristics on the prediction of traction for off-road vehicles

A realistic prediction of the traction capacity of vehicles operating in off-road conditions must account for stochastic variations in the system itself, as well as in the operational environment. Moreover, for mobility studies of wheeled vehicles on deformable soil, the selection of the tire model used in the simulation influences the degree of confidence in the output. Since the same vehicle may carry various loads at different times, it is also of interest to analyze the impact of cargo weight on the vehicle’s traction.This study focuses on the development of an algorithm to calculate the tractive capacity of an off-road vehicle with stochastic vehicle parameters (such as suspension stiffness, suspension damping coefficient, tire stiffness, and tire inflation pressure), operating on soft soil with an uncertain level of moisture, and on a terrain topology that induces rapidly changing external excitations on the vehicle. The analysis of the vehicle–soil dynamics is performed for light cargo and heavy cargo scenarios. The algorithm relies on the comparison of the ground pressure and the calculated critical pressure to decide if the tire can be approximated as a rigid wheel or if it should be modeled as a flexible wheel. It also involves using previously-developed vehicle and stochastic terrain models, and computing the vehicle sinkage, resistance force, tractive force, drawbar pull, and tractive torque.The vehicle model used as a case study has seven degrees of freedom. Each of the four suspension systems is comprised of a nonlinear spring and a viscous (linear or magneto-rheological) damper. An off-road terrain profile is simulated as a 2-D random process using a polynomial chaos approach [Sandu C, Sandu A, Li L. Stochastic modeling of terrain profiles and soil parameters. SAE 2005 transactions. J Commer Vehicles 2005-01-3559]. The soil modeling is concerned with the efficient treatment of the impact of the moisture content on relationships critical in defining the mobility of an off-road vehicle (such as the pressure–sinkage [Sandu C et al., 2005-01-3559] and the shear stress–shear displacement relations). The uncertainties in vehicle parameters and in the terrain profile are propagated through the vehicle model, and the uncertainty in the output of the vehicle model is analyzed [Sandu A, Sandu C, Ahmadian M. Modeling multibody dynamic systems with uncertainties. Part I: theoretical and computational aspects, Multibody system dynamics. Publisher: Springer Netherlands; June 29, 2006. p. 1–23 (23), ISSN: 1384-5640 (Paper) 1573-272X (Online). doi:10.1007/s11044-006-9007-5; Sandu C, Sandu A, Ahmadian M. Modeling multibody dynamic systems with uncertainties. Part II: numerical applications. Multibody system dynamics, vol. 15, No. 3. Publisher: Springer Netherlands; 2006. p. 241–62 (22). ISSN: 1384-5640 (Paper) 1573-272X (Online). doi:10.1007/s11044-006-9008-4]. Such simulations can provide the basis for the study of ride performance, handling, and mobility of the vehicle in rough off-road conditions.     

Efficient generation of accurate mobility maps using machine learning algorithms

U.S Army’s mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles’ capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques (Ahlvin and Haley, 1992; Haley et al., 1979) were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles.The mobility map is a function of different parameters such as terrain topology and profile, soil type (mud, snow, sand, etc.), vegetation, obstacles, weather conditions, and vehicle type and characteristics.A physics-based method such as the discrete element method (DEM) (Dasch et al., 2016) was identified by the NATO Next Generation NRMM Team as a potential high fidelity method to model the soil. This method allows the capture of the soil deformation as well as its non-linear behavior. Hence it allows the simulation of the vehicle on any off-road terrain and have an accurate mobility map generated. The drawback of the DEM method is the required simulation time. It takes several weeks to generate the mobility map because of the large number of soil particles (millions) even while utilizing high performance computing.One approach to reduce the computational time is to use machine learning algorithms to predict the mobility map. Machine learning (Boutell et al., 2004; Burges, 1998; Barber et al., 1997) can lead to very accurate mobility predictions over a wide range of terrains. Machine learning is divided into two categories: the supervised and the unsupervised learning. Supervised learning requires the training data to be labeled into predetermined classes, while the unsupervised learning does not require the training data to be labeled. Machine learning can help generate mobility maps using trained models created from a minimum number of simulation runs. In this study different supervised machine learning algorithms such as the support vector machine (SVM), the nearest neighbor classifier (k-NN), decision trees, and boosting methods were used to create trained models labeled as 2 classes for the ‘go/no-go’ map, 5 classes for the 5-speed map, and 7 classes for the 7-speed map. The trained models were created from the physics-based simulation runs of a nominal wheeled vehicle traversing on a cohesive soil.     

A comparison of ground vehicle mobility analysis based on soil moisture time series datasets from WindSat,LIS, and in situ sensors

Soil moisture is a key terrain variable in ground vehicle off-road mobility. Historically, models of the land water balance have been used to estimate soil moisture. Recently, satellites have provided another source of soil moisture estimates that can be used to estimate soil-limited vehicle mobility. In this study, we compared the off-road vehicle mobility estimates based on three soil moisture sources: WindSat (a satellite source), LIS (a computer model source), and in situ ground sensors (to represent ground truth). Mobility of six vehicles, each with different ranges of sensitivity to soil moisture, was examined in three test sites. The results demonstrated that the effect of the soil moisture error on mobility predictions is complex and may produce very significant errors in off-road mobility analysis for certain combinations of vehicles, seasons, and climates. This is because soil moisture biases vary in both direction and magnitude with season and location. Furthermore, vehicles are sensitive to different ranges of soil moistures. Modeled vehicle speeds in the dry time periods were limited by the interaction between soil traction and the vehicles’ powertrain characteristics. In the wet season, differences in soil strength resulted in more significant differences in mobility predictions.     

Application of the computer simulation model NTVPM-86 to the development of a new version of the infantry fighting vehicle ASCOD

In the past, the task of evaluating soft-ground mobility of off-road vehicles has been carried out primarily using empirical methods (or models), such as the NATO Reference Mobility Model (NRMM) or the Rowland method based on the mean maximum pressure (MMP). The databases for these empirical methods were mostly established decades ago. Consequently, in many cases, they cannot be used in evaluating new generations of vehicles with new design features, as the mobility of these vehicles simply cannot be described within the limits of these empirical databases.Since the 1980s, a series of comprehensive and realistic simulation models for design and performance evaluation of off-road vehicles has emerged. They are based on the detailed studies of the physical nature of vehicle-terrain interaction, taking into account all major vehicle design features and pertinent terrain characteristics. This paper describes the application of one of these models, known as NTVPM-86, developed by Vehicle Systems Development Corporation, Canada, to the design and development of a new version of the ASCOD infantry fighting vehicle, produced by a joint venture formed by Empresa Nacional Santa Barbara of Spain and Steyr-Daimler-Puch of Austria. The results of field tests performed by the Military Technology Agency, Ministry of Defence, Vienna, Austria and released recently confirm that, as predicted by the NTVPM-86 model, the new version of the ASCOD has much improved performance than the original over soft terrain, including soft clay and snow-covered terrain. This is another example of the successful application of the NTVPM-86 model to the design and development of a new generation of high-speed tracked vehicles.     

An optimum design method for robotic tracked-vehicles operating over fresh concrete during straight-line motion

This paper addresses the general problem of the design of tracked base travel systems for special purpose vehicles and/or robotic machines that may be required to move over weak surfaces or over a lightly bonded terrain composed of fresh concrete. For the special case of a vehicle travelling on a very soft fresh concrete during construction, the paper presents detailed comparative studies of the tractive performance of several tracked vehicles with alternative slump values and mean contact pressure configurations. To complete these studies a detailed simulation-analytical method was used. From this, it was established that the simulation analysis method is useful for predicting land locomotion performance of specially designed small tracked vehicles running over fresh concrete of different consistencies during driving and braking action. This work was done for straight-line motion. Some possibilities for the real-time optimum control method of the tractive and braking performance of automated and robotic vehicles are also outlined.     

Computer-aided methods for the optimization of the mobility of single-unit and two-unit articulated tracked vehicles

In the past decade, a series of computer-aided methods (computer-simulation models) have been developed for design and performance evaluation of tracked vehicles, particularly those with short track pitch designed for high speed operations. The latest version, known as NTVPM-86, developed under the auspices of Vehicle Systems Development Corporation, Nepean, Ontario, Canada, takes into account all major vehicle design parameters and terrain characteristics. The basic features of the model have been validated by field tests over a variety of terrains, including mineral, organic and snow-covered terrains. It has been gaining increasingly wide acceptance by industry and governmental agencies in the development and procurement of new vehicles in North America, Europe and Asia. In this paper, the effects of suspension characteristics, initial track tension, track width and ground clearance on the mobility of single unit and two-unit articulated track vehicles over deep snow are systematically evaluated using the computer simulation model NTVPM-86. It is found that these parameters have noticeable effects on vehicle mobility over marginal terrain. The approach to the optimization of tracked vehicle design from the mobility point of view is also examined. It is shown that the simulation model can play a significant role in assisting the procurement manager to select the appropriate vehicle candidates and the design engineer to optimize vehicle design for a given mission and environment.     

Environmental damage from tracked vehicle operation

Soil surface forces resulting from traffic tracked vehicles can cause environmental damage by decreasing plant development and increasing erosion. This paper investigates the soil surface disturbance from tracked vehicle operation. Sharp turns (lower turning radius) from M113 operation produce increased disturbed widths and more severe vegetation damage. The pad-load ratio for the M113 track shoe was determined at various loads. The soil rut produced from tracked vehicle operation was determined at various driving models (straight, smooth turn, sharp turn). The width and depth of track rut and height of soil piled increased when the tracked vehicle negotiated a sharp turn. The results of this study indicate for the soil conditions tested, the width of disturbance is dependent on the operating characteristics of the vehicle. A vehicle conducting sharp turns will disturb a larger width of soil than a vehicle travelling straight or conducting smooth turns.     

Development of a tracked vehicle to study the influence of vehicle parameters on tractive performance in soft terrain

This paper describes a new special tracked vehicle for use in studying the influence of different vehicle parameters on mobility in soft terrain; particularly muskegg and deep snow. A field test in deep snow was carried out to investigate the influence of nominal ground pressure on tractive performance of the vehicle. The vehicle proved useful for studying vehicle parameters influencing the tractive performance of tracked vehicles. The tests show that the nominal ground pressure has a significant effect on the tractive performance of tracked vehicles in deep snow. The decrease in drawbar pull coefficient when the nominal ground pressure is increased and originates at about the same amount from a decrease of the vehicle thrust coefficient, an increase of the belly drag coefficient and an increase of the track motion resistance coefficient.