Is It What I Think It Is? Is It Where I Think It Is? Using Point-Clouds for Diagnostic Testing of a Digging Assembly's Form and Pose for an Autonomous Mining Shovel

Abstract

This paper addresses the problem of verifying a control system's knowledge about the shape and pose of an electric mining shovel's digging assembly. The need for such verification arises in order to ensure safe autonomous operation. The likelihood of unintended collision is reduced by confirming that the digging assembly occupies the region of space it is thought to occupy. We present two methods for verification. The first computes the probability that regions of key interest have the geometric form expected, subject to an allowed uncertainty, given measured point-cloud data. The second computes a likelihood distribution over a family of possible hypotheses by considering the level of support each range measurement provides to each hypothesis. The ideas presented extend, with appropriate adaptation, to other applications where it is necessary to verify the knowledge that a control system may possess about regions of space that are occupied from instant-to-instant.