GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915992845680640 |
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| author | Majd, Keyvan Parwana, Hardik Hoxha, Bardh Hong, Steven Okamoto, Hideki Fainekos, Georgios |
| author_facet | Majd, Keyvan Parwana, Hardik Hoxha, Bardh Hong, Steven Okamoto, Hideki Fainekos, Georgios |
| contents | Articulated vehicles such as tractor-trailers, yard trucks, and similar platforms must often reverse and maneuver in cluttered spaces where pedestrians are present. We present how Barrier-Rate guided Model Predictive Path Integral (BR-MPPI) control can solve navigation in such challenging environments. BR-MPPI embeds Control Barrier Function (CBF) constraints directly into the path-integral update. By steering the importance-sampling distribution toward collision-free, dynamically feasible trajectories, BR-MPPI enhances the exploration strength of MPPI and improves robustness of resulting trajectories. The method is evaluated in the high-fidelity CarMaker simulator on a 12 [m] tractor-trailer tasked with reverse and forward parking in a parking lot. BR-MPPI computes control inputs in above 100 [Hz] on a single GPU (for scenarios with eight obstacles) and maintains better parking clearance than a standard MPPI baseline and an MPPI with collision cost baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems Majd, Keyvan Parwana, Hardik Hoxha, Bardh Hong, Steven Okamoto, Hideki Fainekos, Georgios Robotics Systems and Control Articulated vehicles such as tractor-trailers, yard trucks, and similar platforms must often reverse and maneuver in cluttered spaces where pedestrians are present. We present how Barrier-Rate guided Model Predictive Path Integral (BR-MPPI) control can solve navigation in such challenging environments. BR-MPPI embeds Control Barrier Function (CBF) constraints directly into the path-integral update. By steering the importance-sampling distribution toward collision-free, dynamically feasible trajectories, BR-MPPI enhances the exploration strength of MPPI and improves robustness of resulting trajectories. The method is evaluated in the high-fidelity CarMaker simulator on a 12 [m] tractor-trailer tasked with reverse and forward parking in a parking lot. BR-MPPI computes control inputs in above 100 [Hz] on a single GPU (for scenarios with eight obstacles) and maintains better parking clearance than a standard MPPI baseline and an MPPI with collision cost baseline. |
| title | GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2508.05773 |