Autonomous terrain parameter estimation for wheeled vehicles

This paper reports a methodology for inferring terrain parameters from estimated terrain forces in order to allow wheeled autonomous vehicles to assess mobility in real-time. Terrain force estimation can be used to infer the ability to accelerate, climb, or tow a load independent of the underlying terrain model. When a terrain model is available, physical soil properties and stress distribution parameters that relate to mobility are inferred from vehicle-terrain forces using multiple-model estimation. The approach uses Bayesian statistics to select the most likely terrain parameters from a set of hypotheses, given estimated terrain forces. The hypotheses are based on the extensive literature of soil properties for soils with cohesions from 1 – 70 kPa. Terrain parameter estimation is subject to mathematical uniqueness of the net forces resulting from vehicle-terrain interaction for a given set of terrain parameters; uniqueness properties are characterized in the paper motivating the approach. Terrain force and parameter estimation requires proprioceptive sensors – accelerometers, rate gyros, wheel speeds, motor currents, and ground speed. Simulation results demonstrate efficacy of the method on three terrains – low cohesion sand, sandy loam, and high cohesion clay, with parameter convergence times as low as .02 sec. The method exhibits an ability to interpolate between hypotheses when no single hypothesis adequately characterizes the terrain.