Title :
Autonomous LHD loading
Author :
Petty, Matt K. ; Billingsley, John ; Tran-Cong, Thanh
Author_Institution :
Fac. of Eng. & Surveying, Univ. of Southern Queensland, Qld., Australia
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;
Conference_Titel :
Mechatronics and Machine Vision in Practice, 1997. Proceedings., Fourth Annual Conference on
Conference_Location :
Toowoomba, Qld.
Print_ISBN :
0-8186-8025-3
DOI :
10.1109/MMVIP.1997.625330
