物理情報ニューラルネットワークを用いた自律土工のための土壌特性の現場推定
In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks
物理情報ニューラルネットワーク（PINN）を用いて、土工機械の運動データから土壌の粘着力や内部摩擦角などの特性を推定し、掘削抵抗力を高精度に予測する手法を提案した。
著者: W. Jacob Wagner, Ahmet Soylemezoglu, Dustin Nottage, Katherine Driggs-Campbell
分類: cs.RO
原文アブストラクト
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.
