Automation of earth-moving industries (construction, mining and quarry) require automatic bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket-filling is an open problem since three decades due to difficulties in developing useful earth models (soil, gravel and rock) for automatic control. Operators make use of vision, sound and vestibular feedback to perform the bucket-filling operation with high productivity and fuel efficiency. In this paper, field experiments with a small time-delayed neural network (TDNN) implemented in the bucket control-loop of a Volvo L180H front-end loader filling medium coarse gravel are presented. The total delay time parameter of the TDNN is found to be an important hyperparameter due to the variable delay present in the hydraulics of the wheel-loader. The TDNN network successfully performs the bucket-filling operation after an initial period (100 examples) of imitation learning from an expert operator. The demonstrated solution show only 26% longer bucket-filling time, an improvement over manual tele-operation performance. ; Validerad;2018;Nivå 2;2018-11-07 (johcin)
Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders
2019-01-01
Scopus 2-s2.0-85055696994
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Bucket-wheel loaders in Western Australian ore loading installations
Engineering Index Backfile | 1966
Tema Archive | 2001
RIDE DYNAMICS MODELLING OF MINING BUCKET LOADERS
Taylor & Francis Verlag | 1988
|Application of intelligent systems in modern bucket loaders
Automotive engineering | 1994
|Application of Intelligent Systems in Modern Bucket Loaders
British Library Conference Proceedings | 1994
|