Optimizing Real-Time Performances for Timed-Loop Racing under F1TENTH

TL;DR

This paper introduces a multi-threading approach to enhance real-time control performance in autonomous racing, specifically optimizing Model Predictive Control algorithms on resource-limited embedded systems.

Contribution

It presents a multi-threading technique to improve online control algorithm throughput, reducing latency in resource-constrained autonomous racing platforms.

Findings

Reduced control latency with multi-threading implementation

Improved system throughput for online optimization algorithms

Effective on Nvidia Xavier AGX platform with MPCC

Abstract

Motion planning and control in autonomous car racing are one of the most challenging and safety-critical tasks due to high speed and dynamism. The lower-level control nodes are expected to be highly optimized due to resource constraints of onboard embedded processing units, although there are strict latency requirements. Some of these guarantees can be provided at the application level, such as using ROS2's Real-Time executors. However, the performance can be far from satisfactory as many modern control algorithms (such as Model Predictive Control) rely on solving complicated online optimization problems at each iteration. In this paper, we present a simple yet effective multi-threading technique to optimize the throughput of online-control algorithms for resource-constrained autonomous racing platforms. We achieve this by maintaining a systematic pool of worker threads solving the optimization problem in parallel which can improve the system performance by reducing latency between control input commands. We further demonstrate the effectiveness of our method using the Model Predictive Contouring Control (MPCC) algorithm running on Nvidia's Xavier AGX platform.