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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.

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.

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 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.

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.