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This paper presents a computational framework for the physics-based simulation of light vehicles operating on discrete terrain. The focus is on characterizing through simulation the mobility of vehicles that weigh 1000 pounds or less, such as a reconnaissance robot. The terrain is considered to be deformable and is represented as a collection of bodies of spherical shape. The modeling stage relies on a novel formulation of the frictional contact problem that requires at each time step of the numerical simulation the solution of an optimization problem. The proposed computational framework, when run on ubiquitous Graphics Processing Unit (GPU) cards, allows the simulation of systems in which the terrain is represented by more than 0.5 million bodies leading to problems with more than one million degrees of freedom.

Road loads tire models are used in the automotive industry in full vehicle simulations to compute the loading from the road into the chassis encountered in proving ground durability events. Such events typically include Belgian Block events, bump events, potholes and others. Correctly capturing tire enveloping forces in such events has historically been challenging - several different approaches exist each with its own limitations. In this paper a model is presented which captures the first order tire dynamics (frequencies lower than 80 Hz) and associated enveloping loading without the need of an effective road profile. The theory behind this tire model is briefly introduced. Importantly, a comprehensive study of the validation of the tire model is given which shows correlation for full vehicle dynamic proving ground events. A Virtual Tire Lab (VTL) pre-processing tool is also presented which is used to compute tire model input parameters from a validated non-linear FEA tire model.

The design of vehicles increasingly challenges existing cost, weight, durability, and handling regimes. This challenge is further compounded by pressure to decrease or limit the duration of the design cycle. The simulation of vehicle dynamic behavior commonly applies just rigid, or better rigid and linear flexibility models to predict motions and determine load cases. However, as the boundaries of materials are pushed these are becoming insufficient to accurately predict behavior. Alternatively, complete nonlinear finite element representations of vehicle dynamics are always possible but are presently infeasible for the support of a single design under virtual test, not to mention several design iterations. To address these issues, a novel abstract multi-rate simulation method is outlined which is designed to exploit the richness of available model in the vehicle dynamics domain.

This paper discusses a new Department of Defense (DoD) initiative focused on the development of new generation of MBS computer software that have capabilities and features that are not provided by existing MBS software technology. This three-decade old technology fails to meet new challenges of developing more detailed models in which the effects of significant changes in geometry and large deformations cannot be ignored. New applications require accurate continuum mechanics based vehicle/soil interaction models, belt and chain drive models, efficient and accurate continuum based tire models, cable models used in rescue missions, models that accurately capture large deformations due to thermal and excessive loads, more accurate bio-mechanics models for ligaments, muscles, and soft tissues (LMST), etc.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.