An imitation from observation approach for dozing distance learning in autonomous bulldozer operation

Bulldozers are indispensable heavy equipment for earthwork construction, and improving the intelligence level of bulldozers is of great significance to the construction industry. An efficient autonomous construction of earthmoving machinery requires imitating and learning the expert knowledge of operators under complex environments, and imitation from observation is an effective way. In this work, the expert knowledge of operators was imitated using the proposed hybrid method for rational decision-making of dozing distance, which is one of the key factors affecting the construction efficiency of bulldozers. The proposed method is established based on the modified deep convolutional neural networks (DCNNs) and observation dataset, combined with transfer learning to apply the pre-trained deep learning model to the target task through fine tuning. Comparing the results of different methods reveals that our proposed method obtains the smallest root mean squared error (RMSE) and average error when the expert knowledge of different operators is integrated. The proposed method has universal applicability in solving the observation-based expert knowledge imitation problem. This method also breaks through the imitations of big datasets and computing resource requirements and provides an effective technical route for the practical engineering application of expert knowledge.

Automated summary

Bulldozers are crucial in earthwork construction, and improving their intelligence is significant for the industry. This study proposes a hybrid method that imitates expert knowledge using modified deep convolutional neural networks and observation dataset. The method successfully solves the observation-based expert knowledge imitation problem.