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Simulation-Based Optimization of High-Performance Wheel Loading
Umeå University, Faculty of Science and Technology, Department of Physics. Komatsu Ltd., Japan. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)ORCID iD: 0000-0002-0787-4988
Umeå University, Faculty of Science and Technology, Department of Computing Science. Karlstad University, Sweden.ORCID iD: 0000-0001-8704-9584
2021 (English)In: Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC) / [ed] Chen Feng; Thomas Linner; Ioannis Brilakis, Dubai: International Association for Automation and Robotics in Construction (IAARC) , 2021, p. 688-695Conference paper, Published paper (Refereed)
Abstract [en]

Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines contacting 3D multibody dynamics with a hybrid continuum-particle terrain model, supporting realistic digging forces and soil displacements at real-time performance. A total of 270,000 simulations are run with different loading actions, pile slopes, and soil to analyze how they affect the loading performance. The results suggest that the preferred digging actions should preserve and exploit a steep pile slope. High digging speed favors high productivity, while energy-efficient loading requires a lower dig speed. 

Place, publisher, year, edition, pages
Dubai: International Association for Automation and Robotics in Construction (IAARC) , 2021. p. 688-695
Series
ISARC Proceedings, ISSN 2413-5844
Keywords [en]
Wheel loader, Autonomous, Simulation-Based Optimization, Multibody and soil dynamics
National Category
Robotics and automation Computer Sciences Applied Mechanics
Research subject
Physics; Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-187949DOI: 10.22260/ISARC2021/0093Scopus ID: 2-s2.0-85127564717ISBN: 978-952-69524-1-3 (print)OAI: oai:DiVA.org:umu-187949DiVA, id: diva2:1597689
Conference
ISARC 2021: 38th International Symposium on Automation and Robotics in Construction, Dubai, United Arab Emirates, November 1-5, 2021
Available from: 2021-09-27 Created: 2021-09-27 Last updated: 2025-02-05Bibliographically 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, KojiServin, MartinWadbro, Eddie

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Citation style
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