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.
翻译:自动赛车运动的规划和控制是因高速和动态而最具有挑战性和安全性的任务之一。 低级控制节点预计会由于机载内嵌入式处理器的资源限制而得到高度优化, 尽管有严格的潜伏要求。 其中一些保障可以在应用层面提供, 例如使用 ROS2 的实时执行器。 然而, 性能可能远不能令人满意, 因为许多现代控制算法( 如模型预测控制) 依赖在每次循环中解决复杂的在线优化问题。 在本文中, 我们提出了一个简单而有效的多读技术, 以优化资源限制的自动赛车平台的在线控制算法的通过量。 我们实现这一目标的方法是保持一个系统性的工人线群, 同时解决优化问题, 通过降低控制输入指令之间的惯性来改善系统性能。 我们用Nvidia Xavier AGX 平台运行的模型预测控制算法( MPCC) 进一步证明了我们的方法的有效性。