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This paper describes an approach to integrating high-fidelity vehicle dynamics with a high-fidelity gaming engine, specifically with respect to terrain. The work is motivated by the experimental need to have both high-fidelity visual content with high-fidelity vehicle dynamics to drive a motion base simulator. To utilize a single source of terrain information, the problem requires the just-in-time sharing of terrain content between the gaming engine and the dynamics model. The solution is implemented as a client-server with the gaming engine acting as a stateless server and the dynamics acting as the client. The client is designed to actively maintain a locally cashed terrain grid around the vehicle and actively refresh it by polling the server in an on-demand mode of operation. The paper discusses the overall architecture, the protocol, the server, and the client designs. A practical implementation is described and shown to effectively function in real-time.

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.

Artificial intelligence (AI) enhanced control system deployments are emerging as a viable substitute to more traditional control system. In particular, deep learning techniques offer an alternate approach to tune the ever increasing sets of control system parameters to extract performance. However, the systematic verification and validation (to establish the reliability and robustness) of deep learning based controllers in actual deployments remains a challenge. This is exacerbated by the need to evaluate and optimize control systems embedded within an operational environment (with its own sets of additional unknown or uncertain parameters). Existing literature comparisons of deep learning against traditional controllers, where they may exist, do not offer structured approaches to comparative performance evaluation and improvement. It is also crucial to develop a standardized controlled test environment within which various controllers are evaluated against a common metric.

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.

Laboratory based durability simulation has become an increasingly important component of vehicle system design validation and production release. It offers several advantages over field testing which has driven its adoption in the automotive and military sectors. Among these advantages are 1) repeatability, 2) earlier testing, 3) isolation of subsystems or components and 4) ability to compress and/or accelerate the testing. In this paper we present time-domain methods and techniques adapted, implemented and used at TARDEC to reduce the time required to perform a laboratory durability test of a full vehicle system, subsystem or component. Specifically, these methods approach a durability schedule holistically by considering all events/surfaces, repeats and channels of interest. They employ the standard Generic Stress Life (GSL) approach, utilizing rain flow cycle counting and a minimum-average method of identifying segments of the events which are less severe.

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.

A multi-body dynamics model of the U.S. Army3s High Mobility Multi-purpose Wheeled Vehicle (HMMWV) has been created using commercial software (ADAMS) to simulate and analyze the vehicle3s rollover behavior. However, manual operation of such simulation and analysis for design purposes is prohibitively expensive and time consuming, limiting the engineers3 ability to utilize the model fully and extract from it useful design information in a timely, cost-effective manner. To address this challenge, a commercial system integration and optimization software (OPTIMUS) is utilized in order to automate the simulation processes and to enable the more complex uncertainty-based analysis of the HMMWV rollover behavior under a variety of external conditions. Challenges involved in integrating the software are highlighted and remedies are discussed. Rollover analysis results from using the integrated model and automated simulation are also presented.

The RemoteLink effort supports the U.S. Army's objective for developing and fielding next generation hybrid-electric combat vehicles. It is a distributed soldier-in-the-loop and hardware-in-the-loop environment with a 6-DOF motion base for operator realism, a full-scale combat hybrid electric power system, and an operational context provided by OneSAF. The driver/gunner crewstations rest on one of two 6-DOF motion bases at the U.S. Army TARDEC Simulation Laboratory (TSL). The hybrid power system is located 2,450 miles away at the TARDEC Power and Energy System Integration Laboratory (P&E SIL). The primary technical challenge in the RemoteLink is to operate both laboratories together in real time, coupled over the Internet, to generate a realistic power system duty cycle. A topology has been chosen such that the laboratories have real hardware interacting with simulated components at both locations to guarantee local closed loop stability.

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.

The power system for the Future Combat System's (FCS) family of manned ground vehicles will not only need to satisfy mobility requirements, but also need to provide continuous and pulsed power for weapons, armaments and other auxiliary loads. Investigating hybrid power technologies has been an active research area for the U.S. Army RDECOM's Tank Automotive Research, Development and Engineering Center (TARDEC) Power and Energy System Integration Laboratory (P&E SIL). The P&E SIL is located in Santa Clara, CA and is maintained by Science Applications International Corporation (SAIC). Current P&E SIL efforts include imposing realistic loads on notional combat vehicle subsystems in order to evaluate components, such as motors and batteries.

This paper presents an approach to full-vehicle simulator control which accounts for nonlinearities in a vehicle/simulator system. The control scheme presented is based on the estimation of the system inverse dynamics. A composite linear/nonlinear approach to inverse system identification (SYS-ID) is presented. The linear portion of the SYS-ID uses time-domain methods to estimate the impulse response of the inverse system in a least squares sense. These results are then extended by using the regularized approach to least squares estimation. The nonlinear part uses the support vector machine to approximate the nonlinear deviations from the linear model. Two approaches to using this composite model are presented. Examples of the linear SYS-ID techniques are shown for a 2×2 system.

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 response to several drawbar failures, which occurred on a military trailer negotiating rough cross-country terrain, TARDEC engineers analyzed, identified, corrected and tested an in-field repair kit using the tools of analytical and physical simulation. Using both the field-test data and simulator response data, a finite element analysis (FEA) model of the trailer frame and drawbar structure was developed. The FEA model showed several weak areas in the riveted Aluminum structure of the military trailer. The model was then used to evaluate recommended design changes to the trailer frame structure and drawbar. TARDEC’s Pintle Motion Base Simulator (PMBS) was used to validate the design changes in prototype. By iterating the process between the FEA model and the PMBS, a robust, cost effective field repair kit was developed and shown to be durable using both analytical and physical simulation methods in a cooperative effort.