Classication framework formonitoring calibration ofautonomous waist-actuated minevehicles

Abstract: For autonomous mine vehicles that perform the ”load-haul-dump” (LHD) cycle to operate properly, calibration of the sensors they rely on is crucial. The LHD cycle refers to a vehicle that loads material, hauls the material along a route and dumps it in an extraction point. Many of these vehicles are waist-actuated, meaning that the front and rear part of the machines are fixated at an articulation point.   The focus of this thesis is about developing and implementing two differ- ent frameworks to distinguish patterns from routes where calibration of the hinge-angle sensor was needed before and try to predict when calibrating the sensor is needed. We present comparative results of one method using ma- chine learning, specifically supervised learning with support vector machine and one optimization-based method using scan matching by implementing a two-dimensional NDT (Normal Distributions Transform) algorithm.   Comparative results based on evaluation metrics used in this thesis show that detecting incorrect behaviour of the hinge-angle sensor is possible. Evaluation show that the machine learning classifier performs better on the data used for this thesis than the optimization-based classifier.