An individual prediction model of the pre-loading motion for operator and backhoe pairs

Abstract

The autonomous dump truck must judge whether the backhoe is ready for loading the sediment for smooth and safe cooperation with the human-operated backhoes. Analyzing time-series data of the human-operated backhoe loading motion is an effective method to build a prediction model. The transition of several primitive motions enables us to predict the timing when the backhoe starts loading. However, in transition modeling, manually selecting the appropriate primitive motions in the pre-loading motions requires considerable effort. In addition, a robust loading prediction is required for sensor layout changes in the installations of them. Here, we propose a BP-HMM-based prediction method of the pre-loading motion. The algorithm automatically finds the transition of several primitive motions from time-series data and its annotation labels. The selection of three angular velocities as features of the BP-HMM increases the robustness of the prediction method. The proposed method built a suitable prediction model for three different combinations of operators and backhoes. The prediction method was robust for sensor layout changes, and showed an accuracy of 100%. The proposed prediction method contributes to the automation of earthmoving work by enabling smooth cooperation between autonomous dump trucks and human-operated backhoe.