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Optimizing wheel loader performance: an end-to-end approach
Umeå University, Faculty of Science and Technology, Department of Physics. Komatsu Ltd.. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Computing Science. Karlstad University.ORCID iD: 0000-0001-8704-9584
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)ORCID iD: 0000-0002-0787-4988
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. This task presents a challenging optimization problem since each loading’s performance depends on the pile state, which depends on previous loadings. We investigate an end-toend optimization approach considering future loading outcomes and V-cycle transportation costs. To predict the evolution of the pile state and the loading performance, we use world models that leverage deep neural networks trained on numerous simulated loading cycles. A look-ahead tree search optimizes the sequence of loading actions by evaluating the performance of thousands of action candidates, which expand into subsequent action candidates under the predicted pile states recursively. Test results demonstrate that, over a horizon of 15 sequential loadings, the look-ahead tree search is 6 % more efficient than a greedy strategy, which always selects the action that maximizes the current single loading performance, and 14 % more efficient than using a fixed loading controller optimized for the nominal case.

National Category
Computer Sciences Computational Mathematics Other Physics Topics
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-233065OAI: oai:DiVA.org:umu-233065DiVA, id: diva2:1922525
Available from: 2024-12-18 Created: 2024-12-18 Last updated: 2024-12-30Bibliographically approved
In thesis
1. High-performance autonomous wheel loading: a computational approach
Open this publication in new window or tab >>High-performance autonomous wheel loading: a computational approach
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Högpresterande autonom hjullastning : en beräkningsmetod
Abstract [en]

Smart and autonomous earthmoving equipment enhances energy efficiency,productivity, and safety at construction sites and mines. The innovations provide means to reach high-set sustainability goals and be profitable despite increasing labor shortages. In addition, recent technological breakthroughs in artificial intelligence highlight the potential of superhuman capabilities to further enhance operations. This thesis presents a computational approach to end-to-end optimization of autonomous wheel loaders operating in a dynamic environment. Wheel loaders are mainly used for repeatedly loading material and carrying it to load receivers in quarries and mines. The difficulty lies in that each loading action alters the state of the material pile. The resulting state affects the possible outcomes of the subsequent loading process and, ultimately, the total performance. Thus, the challenge is to achieve both autonomous and high-performance wheel loading over a sequence of tasks. Achieving this requires the ability to predict future outcomes and account for the cumulative effect of loading actions. The thesis constructs a real-time wheel loader simulator, develops world models for sequential loading actions with evolving pile states, formulates the end-to-end optimization problem, and introduces a look-ahead tree search method to solve the problem. These contributions provide insights into utilizing physics-based simulation in combination with machine learning to further improve sustainability in mining and construction.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2025. p. 31
Keywords
Earthmoving, Automation, Wheel loader, Bucket-filling, Multibody and soil dynamics, Realtime simulation, Sim-to-real gap, World modeling, Deep learning, Optimization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-233090 (URN)978-91-8070-567-7 (ISBN)978-91-8070-568-4 (ISBN)
Public defence
2025-01-24, MIT.A.121, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2025-01-07 Created: 2024-12-20 Last updated: 2025-01-07Bibliographically approved

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Aoshima, KojiWadbro, EddieServin, Martin

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CiteExportLink to record
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