Estimating Off-Road Ground Contact Forces for a Real Time Motion Simulator

• Main Authors: Richmond, Paul W, Jones, Randolph A, Creighton, Daniel C, Ahlvin, Richard B
• Format: Report
• Language: English
• ISSN: 0148-7191; 2688-3627
• DOI: 10.4271/2004-01-2643

Researchers at the US Army Engineer Research and Development Center (ERDC), working in the field of vehicle mobility, have developed methods to predict the physical interactions of vehicles with soil surfaces. This set of methods use research conducted at the ERDC over the last 40 years to predict the physical interactions of vehicles and terrain surfaces under all seasons. Methodologies to measure pertinent terrain properties and assess vehicle performance have also been developed. Much of the work focused on practical applications and is the result of extensive vehicle performance testing and the subsequent analysis of the test results. While there have been attempts to identify and characterize soil media properties using standard soil classification techniques and to assess their effects on vehicle mobility using classical soil mechanics and other theoretical approaches, the current state-of-the-art is such that these approaches have limited practical application. The ERDC's approach has been to quantify and relate a set of fundamental vehicle traction element performances to easily obtainable terrain properties. This empirical and lumped parameter approach has provided relevant and verifiable results for decades, but due to its nature, is difficult to extrapolate and manipulate mathematically. Nevertheless, the ease of obtaining suitable terrain properties, its historical success in vehicle performance predictions, and its maturity and broad validation database have made it an attractive choice for implementation into a simulated terrain mechanics model for real time dynamics vehicle simulators. This paper describes the algorithms selected for estimating longitudinal force coefficients for soils (frozen and non-frozen), snow, and ice, and the underlying assumptions.