It is widely recognized that simulation is pivotal to vehicle development, whether manned or unmanned. There are few
dedicated choices, however, for those wishing to perform realistic, end-to-end simulations of unmanned ground vehicles
(UGVs). The Virtual Autonomous Navigation Environment (VANE), under development by US Army Engineer
Research and Development Center (ERDC), provides such capabilities but utilizes a High Performance Computing
(HPC) Computational Testbed (CTB) and is not intended for on-line, real-time performance. A product of the VANE
HPC research is a real-time desktop simulation application under development by the authors that provides a portal into
the HPC environment as well as interaction with wider-scope semi-automated force simulations (e.g. OneSAF). This
VANE desktop application, dubbed the Autonomous Navigation Virtual Environment Laboratory (ANVEL), enables
analysis and testing of autonomous vehicle dynamics and terrain/obstacle interaction in real-time with the capability to
interact within the HPC constructive geo-environmental CTB for high fidelity sensor evaluations. ANVEL leverages
rigorous physics-based vehicle and vehicle-terrain interaction models in conjunction with high-quality, multimedia
visualization techniques to form an intuitive, accurate engineering tool. The system provides an adaptable and
customizable simulation platform that allows developers a controlled, repeatable testbed for advanced simulations.
ANVEL leverages several key technologies not common to traditional engineering simulators, including techniques
from the commercial video-game industry. These enable ANVEL to run on inexpensive commercial, off-the-shelf
(COTS) hardware. In this paper, the authors describe key aspects of ANVEL and its development, as well as several
initial applications of the system.
The propulsion systems employed on unmanned ground vehicle platforms in Future Force Units of Action will likely involve electric or hybrid-electric drive. Power storage systems for these platforms will therefore be driven largely by expected power depletion rates. Resistances that propulsion systems must overcome during maneuvers will be a major factor affecting power depletion rates, and the resistance forces will vary drastically depending on the mission. Therefore, realistic mission-related considerations need to be applied when defining power storage requirements. The US Army has developed numerous models and simulations that use terra-mechanics algorithms to predict maneuver capability for ground vehicles as limited by terrain and environmental factors, and the algorithms employed for predicting maneuver capability in most of these models and simulations are founded on the terra-mechanics algorithms contained in the NATO Reference Mobility Model. The NATO Reference Mobility Model uses physics-based force balancing algorithms with terra-mechanics relationships that were empirically derived from decades of vehicle-terrain interaction research, and it also incorporates proven methodologies for assessing mission effectiveness in terms of maneuver capabilities. The terra-mechanics algorithms and methodologies for assessing mission effectiveness that are implemented in this model and in other related software tools, such as those used for route analysis, can be used to generate realistic mission-related resistance profiles for defining power storage requirements.
The USA Engineer Research and Development Center (ERDC) has conducted on-/off-road experimental field testing with full-sized and scale-model military vehicles for more than fifty years. Some 4000 acres of local terrain are available for tailored field evaluations or verification/validation of future robotic designs in a variety of climatic regimes. Field testing and data collection procedures, as well as techniques for quantifying terrain in engineering terms, have been developed and refined into algorithms and models for predicting vehicle-terrain interactions and resulting forces or speeds of military-sized vehicles. Based on recent experiments with Matilda, Talon, and Pacbot, these predictive capabilities appear to be relevant to most robotic systems currently in development. Utilization of current testing capabilities with sensor-based vehicle drivers, or use of the procedures for terrain quantification from sensor data, would immediately apply some fifty years of historical knowledge to the development, refinement, and implementation of future robotic systems. Additionally, translation of sensor-collected terrain data into engineering terms would allow assessment of robotic performance a priori deployment of the actual system and ensure maximum system performance in the theater of operation.