GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems

TL;DR

This paper introduces BR-MPPI, a GPU-accelerated control method embedding Control Barrier Functions into MPPI to enable safe, efficient navigation of articulated vehicles in cluttered environments.

Contribution

The paper presents a novel Barrier-Rate guided MPPI approach that integrates CBF constraints into the path integral framework for improved obstacle avoidance.

Findings

BR-MPPI runs at over 100 Hz on a single GPU.

It achieves better parking clearance than standard MPPI.

It effectively navigates tractor-trailers in complex scenarios.

Abstract

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.