Blade Control for Bulk Dozer Push by Bulldozer using Reinforcement Learning

Bulldozers in mines are used for bulk dozer push. In this operation, the bulldozers push unwanted soil covering ore off cliffs. Conventional rule-based blade control methods predetermine the excavation depth, leading to suboptimal performance in each environment. This paper presents a novel blade control method using deep reinforcement learning for the operation. The proposed model takes vehicle information (e.g. pose, speed, engine speed) and terrain information as inputs and outputs blade commands. In the training, the model is given a higher reward when the agent dumps a larger amount of soil in a shorter time. This reward function encourages the agent to achieve efficient work. The proposed method is trained and evaluated in a physics simulator which simulates interactions between a bulldozer and soil. The simulation includes the powertrain characteristics of the bulldozer, which is important for bulk dozer push. To show the proposed method outperforms a conventional rule-based control method in productivity, experiments are performed in the evaluation.