挑战专业赛车手的整体运动规划和控制解决方案
A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver
摘要：
我们提出了一个整体设计的三层控制架构，能够超越专业车手赛车相同的汽车。我们的方法侧重于运动规划和控制层的协同设计，充分挖掘互联系统的潜力。首先，高级规划器计算围绕轨道的最优轨迹，然后在实时中，中级非线性模型预测控制器使用高级信息作为指导遵循该路径。最后，一个高频率，低级别的控制器跟踪由中间级别的控制器预测的状态。跟踪预测的行为有两个优点：它减少了上层使用的模型和真实汽车之间的不匹配，并允许更高级别的运动规划器优化扭矩矢量控制命令。事实证明，底层控制器的定制设计对于弥合规划和控制之间的差距至关重要，并在自动驾驶赛车中释放出看不见的性能。所提出的方法在全尺寸赛车上进行了验证，大大提高了在同一车辆上取得的最先进的结果。最后，我们还表明，所提出的协同设计方法优于专业赛车手。
英文摘要：
We present a holistically designed three layer control architecture capable of outperforming a professional driver racing the same car. Our approach focuses on the co-design of the motion planning and control layers, extracting the full potential of the connected system. First, a high-level planner computes an optimal trajectory around the track, then in real-time a mid-level nonlinear model predictive controller follows this path using the high-level information as guidance. Finally a high frequency, low-level controller tracks the states predicted by the mid-level controller. Tracking the predicted behavior has two advantages: it reduces the mismatch between the model used in the upper layers and the real car, and allows for a torque vectoring command to be optimized by the higher level motion planners. The tailored design of the low-level controller proved to be crucial for bridging the gap between planning and control, unlocking unseen performance in autonomous racing. The proposed approach was verified on a full size racecar, considerably improving over the state-of-the-art results achieved on the same vehicle. Finally, we also show that the proposed co-design approach outperforms a professional racecar driver.
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