Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10642 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914035640827904 |
|---|---|
| author | Abdolmohammadi, Armin Mojahed, Navid Ravani, Bahram Nazari, Shima |
| author_facet | Abdolmohammadi, Armin Mojahed, Navid Ravani, Bahram Nazari, Shima |
| contents | Earthmoving operations with wheel loaders require substantial power and incur high operational costs. This work presents an efficient automation framework based on the Fundamental Earthmoving Equation (FEE) for soil-tool interaction modeling. A reduced-order multi-step parameter estimation method guided by Sobol's global sensitivity analysis is deployed for accurate, online excavation force prediction. An optimal control problem is then formulated to compute energy-efficient bucket trajectories using soil parameters identified in the previous digging cycle. High-fidelity simulations in Algoryx Dynamics confirm accurate force prediction and demonstrate 15-40% energy savings compared to standard paths. The total computation time is comparable to a single digging cycle, highlighting the framework's potential for real-time, energy-optimized wheel loader automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling Abdolmohammadi, Armin Mojahed, Navid Ravani, Bahram Nazari, Shima Systems and Control Earthmoving operations with wheel loaders require substantial power and incur high operational costs. This work presents an efficient automation framework based on the Fundamental Earthmoving Equation (FEE) for soil-tool interaction modeling. A reduced-order multi-step parameter estimation method guided by Sobol's global sensitivity analysis is deployed for accurate, online excavation force prediction. An optimal control problem is then formulated to compute energy-efficient bucket trajectories using soil parameters identified in the previous digging cycle. High-fidelity simulations in Algoryx Dynamics confirm accurate force prediction and demonstrate 15-40% energy savings compared to standard paths. The total computation time is comparable to a single digging cycle, highlighting the framework's potential for real-time, energy-optimized wheel loader automation. |
| title | Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.10642 |