LSTM-based Prediction of Excavating Resistive Force Using Bucket Trajectory Shape

Abstract

Unmanned construction machine has become a promising technology in the civil engineering field. The machine is still less efficient, especially in excavating soil, than the manned construction because the unmanned machine works carefully to avoid potential hazards due to unpredicted soil deformation as well as excavation force. This research aims to predict time-series resistive force of bucket during soil excavation based on the Recurrent Neural Network. First, we experimentally obtain excavating resistive force for various excavation trajectories. In the experiment, a bucket follows pre-determined trajectories with four different elliptical arcs, and the resistive force acting on the bucket is measured. The prediction model of the resistive force is then elaborated by indexing the bucket trajectories. The model validation confirms that the aspect ratio of the ellipse of the trajectories is an effective index for accurately predicting the excavating resistive force.