Autonomous LHD loading

Author

Petty, Matt K. ; Billingsley, John ; Tran-Cong, Thanh

Author_Institution

Fac. of Eng. & Surveying, Univ. of Southern Queensland, Qld., Australia

fYear

1997

fDate

23-25 Sep 1997

Firstpage

219

Lastpage

224

Abstract

Machine vision is used for guidance of the autonomous loading of ore during underground mining. Three dimensional spatial data of the ore pile is derived in real-time from camera images and is used for planning the scooping process. A sensory integration technique combines feedforward from the same vision system with wheel odometry to guide the vehicle to and from the ore pile. A computationally efficient kinematic model of the vehicle is derived and its application discussed. LHD (load-haul-dump) vehicles are used extensively in underground mining. Increasing production costs and the ongoing quest for improved safety provide a great incentive to automate their working cycle. This research concentrates on a major component of this task-automation of loading. The proposed loading controller will load an LHD swiftly and safely while leaving the ore pile in a suitable condition for subsequent bucket scoops

Keywords

CCD image sensors; computer vision; excavators; feedforward; kinematics; materials handling; mining; autonomous loading; camera images; computationally efficient kinematic model; feedforward; load-haul-dump vehicles; machine vision; sensory integration technique; underground mining; wheel odometry; Cameras; Costs; Kinematics; Machine vision; Navigation; Ores; Process planning; Production; Remotely operated vehicles; Wheels;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Machine Vision in Practice, 1997. Proceedings., Fourth Annual Conference on

Conference_Location

Toowoomba, Qld.

Print_ISBN

0-8186-8025-3

Type

conf

DOI

10.1109/MMVIP.1997.625330

Filename

625330