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Machine Learning approach to Automatic Bucket Loading
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-7395-7557
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Number of Authors: 42016 (English)In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 1260-1265, article id 7535925Conference paper, Published paper (Refereed)
Abstract [en]

The automation of bucket loading for repetitive tasks of earth-moving operations is desired in several applications at mining sites, quarries and construction sites where larger amounts of gravel and fragmented rock are to be moved. In load and carry cycles the average bucket weight is the dominating performance parameter, while fuel efficiency and loading time also come into play with short loading cycles. This paper presents the analysis of data recorded during loading of different types of gravel piles with a Volvo L110G wheel loader. Regression models of lift and tilt actions are fitted to the behavior of an expert driver for a gravel pile. We present linear regression models for lift and tilt action that explain most of the variance in the recorded data and outline a learning approach for solving the automatic bucket loading problem. A general solution should provide good performance in terms of average bucket weight, cycle time of loading and fuel efficiency for different types of material and pile geometries. We propose that a reinforcement learning approach can be used to further refine models fitted to the behavior of expert drivers, and we briefly discuss the scooping problem in terms of a Markov decision process and possible value functions and policy iteration schemes.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016. p. 1260-1265, article id 7535925
Series
Mediterranean Conference on Control and Automation, E-ISSN 2325-369X
Keywords [en]
Information technology - Automatic control
Keywords [sv]
Informationsteknik - Reglerteknik
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology Control Engineering
Research subject
Industrial Electronics; Mobile and Pervasive Computing; Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-28755DOI: 10.1109/MED.2016.7535925ISI: 000391154900208Scopus ID: 2-s2.0-84986226543Local ID: 2a83ec63-323c-43a5-a69f-f77d7f9abfb0ISBN: 978-1-4673-8345-5 (electronic)OAI: oai:DiVA.org:ltu-28755DiVA, id: diva2:1001959
Conference
Mediterranean Conference on Control and Automation : 21/06/2016 - 24/06/2016
Note

Godkänd; 2016; 20160819 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-06Bibliographically approved
In thesis
1. Automation of Wheel-Loaders
Open this publication in new window or tab >>Automation of Wheel-Loaders
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Automation av hjullastare
Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-71460 (URN)978-91-7790-258-4 (ISBN)978-91-7790-259-1 (ISBN)
Public defence
2019-01-31, A1545, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-11-21Bibliographically approved

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Dadhich, SiddharthBodin, UlfSandin, FredrikAndersson, Ulf

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