论文标题
基于优化的自主赛车层次运动计划
Optimization-Based Hierarchical Motion Planning for Autonomous Racing
论文作者
论文摘要
在本文中，我们提出了一个用于自动赛车的分层控制器，其中在两级优化框架中使用相同的车辆模型进行运动计划。高级控制器计算一个轨迹，该轨迹最小化了圈速时间，而控制器之后的低级非线性模型预测路径在线轨迹轨迹。遵循计算的最佳轨迹避免在线计划并实现快速的计算时间。通过在高级控制器中计算的终端约束，通过两个级别的耦合进一步提高了效率。在实时优化级别中包括此约束，可确保可以缩短预测范围，同时保证安全性。事实证明，对于全尺寸无人驾驶赛车的实验验证至关重要。有关车辆使用拟议框架赢得了两次国际学生赛车比赛；此外，与使用非常相似的汽车和轨道达到的最新结果相比，我们的分层控制器在单圈时间内取得了20％的提高。
In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.
