Imitation-based control of automated ore excavator: improvement of autonomous excavation database quality using clustering and association analysis processes

Abstract

<p>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 excavation motions depending on the fragmented rock pile condition. In our previous work, we have proposed an imitation-based motion planning method and developed a recognizer of the rock pile condition and a motion planner. Experimental results using a 1/10-scale excavation model have demonstrated the fundamental feasibility. They have also revealed that both the number and diversity of learning data must be considered to achieve high-productive excavation. In this paper, to use learning data more effectively and to diversify the autonomous excavation database, clustering and association analysis processes are applied to the learning data. We propose a method to improve the database quality using results of those processes. The method merges two bipolar compositions; one follows natural conditions and the other is modified intentionally based on the occurrence and usage frequency. The experiment verifies the effectiveness of the proposed method and clarifies that the database contents can be adjusted based on the occurrence frequency of rock pile conditions and usage frequency of the excavation motions.</p