Imitation-based control of automated ore excavator to utilize human operator knowledge of bedrock condition estimation and excavating motion selection

In order to perform productive autonomous excavation of a fragmented rock pile, it is necessary to recognize the condition of the fragmented rock pile and to plan appropriate excavating motion according to the condition of the fragmented rock pile. In this paper, we propose imitation-based motion planning method, and develop a recognizer of rock pile condition and an excavating motion planner. We also develop an 1/10-scale excavation model and conduct excavation experiment. In the experiment, the proposed method works well from the view point of productivity. It is confirmed that the fragmented rock pile condition can be described by its shape and the particle size distribution of its surface. The proposed approach has feasibility in autonomous excavation of the fragmented rock pile. Experimental results also reveal that both the number of learning data and the diversity of learning data are important to realize a high-productive excavation.