  A novel, learning-based method for in situ estimation of soil properties
using a physics-infused neural network (PINN) is presented. The network is
trained to produce estimates of soil cohesion, angle of internal friction,
soil-tool friction, soil failure angle, and residual depth of cut which are
then passed through an earthmoving model based on the fundamental equation of
earthmoving (FEE) to produce an estimated force. The network ingests a short
history of kinematic observations along with past control commands and predicts
interaction forces accurately with average error of less than 2kN, 13% of the
measured force. To validate the approach, an earthmoving simulation of a bladed
vehicle is developed using Vortex Studio, enabling comparison of the estimated
parameters to pseudo-ground-truth values which is challenging in real-world
experiments. The proposed approach is shown to enable accurate estimation of
interaction forces and produces meaningful parameter estimates even when the
model and the environmental physics deviate substantially.

Robotics