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This is a Quantitative Study study.

October 13, 2025Open Access

Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling

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Authors

AAArmin Abdolmohammadi NMNavid Mojahed BRBahram RavaniUniversity of California, Davis

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Implication

This work demonstrates automation strategies to optimize energy usage in wheel loaders, highlighting effective soil-tool interaction modeling and optimal control methods.

Key Points

Energy-efficient bucket trajectories lead to 15-40% savings compared to standard paths.
Accurate excavation force predictions are achieved using soil parameters from previous digging cycles.
The framework employs Sobol's global sensitivity analysis for improved parameter estimation.
High-fidelity simulations validate the framework's effectiveness within real-time constraints.

Cite This Study

Abdolmohammadi et al. (2025) studied this question.

synapsesocial.com/papers/68ed1896f29694dd1da78cb6 https://doi.org/10.48550/arxiv.2509.10642

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Implication

This work demonstrates automation strategies to optimize energy usage in wheel loaders, highlighting effective soil-tool interaction modeling and optimal control methods.

Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling

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Member takes

Implication

Key Points

Cite This Study

Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling

Discussion

Member takes

Implication

Key Points

Cite This Study

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