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This paper describes a human-in-the-loop motion-based simulator which was designed, built and used to measure the duty cycle of a combat vehicle in a virtual simulation environment. The simulation environment integrates two advanced crewstations which implement both a driver's station and a gunner's station of a simulated future tank. The simulated systems of the tank include a series hybrid-electric propulsion system and its main weapon systems. The simulated vehicle was placed in a virtual combat scenario which was then executed by the participating Soldiers. The duty cycle as measured includes the commands of the driver and gunner as well as external factors such as terrain and enemy contact. After introducing the project, the paper describes the simulation environment which was assembled to run the experiment. It emphasizes the design of the experiment as well as the approach, challenges and issues involved.

This paper describes a human-in-the-loop motion-based simulator which was built to perform controlled fuel economy measurements for both a conventional and hybrid electric HMMWV. The simulator was constructed with a driver's console, visualization system, and audio system all of which were mounted on the motion base simulator. These interface devices were then integrated with a real-time dynamics model of the HMMWV. The HMMWV dynamics model was built using the real-time vehicle modeling tool SimCreator®, which, in turn was integrated with two powertrain models implemented with Gamma Technologies GT-Drive product. These two powertrains consisted of a conventional configuration and a series hybrid-electric configuration. They were then run on four different standard Army fuel consumption courses to replicate tests which had previously been conducted at the proving ground. Experiments were performed for varying speeds with two experienced proving ground drivers.

In this paper a yaw stability control algorithm along with an emergency roll control strategy have been developed. The yaw stability controller and emergency roll controller were both developed using linear two degree-of-freedom vehicle models. The yaw stability controller is based on Lyapunov stability criteria and uses vehicle lateral acceleration and yaw rate measurements to calculate the corrective yaw moment required to stabilize the vehicle yaw motion. The corrective yaw moment is then applied by means of a differential braking strategy in which one wheel is selected to be braked with appropriate brake torque applied. The emergency roll control strategy is based on a rollover coefficient related to vehicle static stability factor. The emergency roll control strategy utilizes vehicle lateral acceleration measurements to calculate the roll coefficient. If the roll coefficient exceeds some predetermined threshold value the emergency roll control strategy will deploy.

A Vehicle Dynamics and Mobility Server (VDMS) is being developed by the U.S. Army to perform real-time high-fidelity simulation of robotic vehicle concepts. It allows a set of conceptual Unmanned Ground Vehicles (UGVs) to be selected and configured for the purposes of evaluating their mobility performance in a simulated battlefield scenario. VDMS includes real-time ground vehicle models operating over high-resolution digital terrain. The models consist of three-dimensional multi-body vehicle dynamics, off-road vehicle-soil interaction, collision detection and obstacle negotiation code, and autonomous control algorithms. A minimally completed VDMS was used in an RDEC Federation Calibration Experiment (CalEx) in October 2001 to predict the mobility of ten robotic scout vehicles. This paper presents the rationale, requirements, design, and implementation of VDMS. It also briefly discusses other possible applications of VDMS and the future direction of VDMS.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

In this paper, a distributed driver-in-the-loop and hardware-in-the-loop simulator is described with a driver on a motion simulator at the U.S. Army TARDEC Ground Vehicle Simulation Laboratory (GVSL). Realistic power system response is achieved by linking the driver in the GVSL with a full-sized hybrid electric power system located 2,450 miles away at the TARDEC Power and Energy Systems Integration Laboratory (P&E SIL), which is developed and maintained by Science Applications International Corporation (SAIC). The goal is to close the loop between the GVSL and P&E SIL over the Internet to provide a realistic driving experience in addition to realistic power system results. In order to preserve a valid and safe hardware-in-the-loop experiment, the states of the GVSL must track the states of the P&E SIL.

In the context of off-road vehicle simulations, deformable terrain models mostly fall into three categories: simple visualization of the deformed terrain only, use of empirical relationships for the deformation, or finite/discrete element approaches for the terrain. A real-time vehicle dynamics simulation with a physics-based tire model (brush, ring or beam-based models) requires a terrain model that accurately reflects the deformation and response of the soil to all possible inputs of the tire in order to correctly simulate the response of the vehicle. The real-time requirement makes complex finite/discrete element approaches unfeasible, and the use of a ring or beam -based tire model excludes purely empirical terrain models. We present the development of a three-dimensional vehicle/terrain interaction model which is comprised of a tire and deformable terrain model to be used with a real-time vehicle dynamics simulator.

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.

The vertical force generated from terrain-tire interaction has long been of interest for vehicle dynamic simulations and chassis development. To improve simulation efficiency while still providing reliable load prediction, a terrain pre-filtering technique using a constraint mode tire model is developed. The wheel is assumed to convey one quarter of the vehicle load constantly. At each location along the tire's path, the wheel center height is adjusted until the spindle load reaches the pre-designated load. The resultant vertical trajectory of the wheel center can be used as an equivalent terrain profile input to a simplified tire model. During iterative simulations, the filtered terrain profile, coupled with a simple point follower tire model is used to predict the spindle force. The same vehicle dynamic simulation system coupled with constraint mode tire model is built to generate reference forces.