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Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

Strategies for evaluating the impact of mission requirements on the design of mission-specific vehicles are needed to enable project managers to assess potential benefits and associated costs of changes in requirements. Top-level requirements that cause significant cascaded difficulties on lower-level requirements should be identified and presented to decision-makers. This paper aims to introduce formal methods and computational tools to enable the analysis and allocation of mission requirements.

The complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

Ground vehicle subjecting to a blast can sustain vehicle damages and occupant injuries. Direct blast thermal and force loadings compromise vehicle structural integrity and cause damages. Computer simulations of vehicle blast wave damages can be obtained by solving the gas dynamics of the blast wave and the structural dynamics of the vehicle, through a projection of the wave's impact on the vehicle structure. There are various possible ways that the blast can cause injuries to the vehicle occupants, such as direct collision with objects instantly accelerated by the blast pressure and impact by the secondary shock waves transmitted through the platform structure. This paper describes a parallel computer simulation methodology that can potentially be applied to predict the structure damage and the associated occupant kinematics during a blast event by solving the multi-physics problem of fluid dynamics, solid dynamics, and multi-body dynamics.

The Army continues to improve its Reliability-based Design Optimization (RBDO) process, expanding from component optimization to system optimization. We are using the massively parallel computing power of the Department of Defense (DoD) High Performance Computing (HPC) systems to simultaneously optimize multiple components which interact with each other in a mechanical system. Specifically, we have a subsystem of a military ground vehicle, consisting of more than four components and are simultaneously optimizing five components of that subsystem using RBDO methods. We do not simply optimize one component at a time, sequentially, and iterate until convergence. We actually simultaneously optimize all components together. This can be done efficiently using the parallel computing environment. We will discuss the results of this optimization, and the advantages and disadvantages of using HPC systems for this work.

This paper addresses the issue of thermal modeling of vehicle components where the 3D models of the components are not traditional CAD models derived from engineering drawings but are models derived from 3D-imaging scans of existing real-world objects. A “reverse engineering” pipeline is presented that uses 3D scanners to capture the geometry of an existing object from different views and then integrates these multiple views into a single 3D surface mesh description of the object. This process requires no a priori CAD drawings of the object and thus enables modeling in situations where the original manufacturer no longer exists or soldiers have made undocumented field modifications. The paper further discusses the use of these generated 3D models to simulate thermal imaging properties of the object using the Multi Service Electro-Optic Signature (MuSES) software.

The U.S. Army is seeking to develop autonomous off-road mobile robots to perform tasks in the field such as supply delivery and reconnaissance in dangerous territory. A key problem to be solved with these robots is off-road mobility. We have developed a computer model of one concept robot, the “T1” omnidirectional vehicle (ODV), to study the effects of different control strategies on the robot's off-road mobility. The T1 is a lightweight robot with an innovative running-gear and control strategy to enhance mobility characteristics. We built the dynamic model of T1 in ADAMS/Car and the control system in MATLAB/Simulink. This paper presents the template-based method used to construct the ADAMS model of the T1 ODV. It also discusses effective linking of ADAMS and MATLAB for control system development. Finally, this paper includes a section describing the extension of the T1 templates to other similar ODV concepts for rapid development.