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

Phillips, T. G., Green, M. E. and McAree, P. R. (2016) 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. Journal of Field Robotics, 33 7: 1013-1033. doi:10.1002/rob.21643


Author Phillips, T. G.
Green, M. E.
McAree, P. R.
Title 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
Journal name Journal of Field Robotics   Check publisher's open access policy
ISSN 1556-4967
1556-4959
Publication date 2016-01-01
Sub-type Article (original research)
DOI 10.1002/rob.21643
Open Access Status Not Open Access
Volume 33
Issue 7
Start page 1013
End page 1033
Total pages 21
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
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.
Keyword Control systems
Diagnostic testing
Autonomous operation
Mining equipment
Mining shovels
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
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