论文标题
学习模型的预测控制对竞争性自主赛车
Learning Model Predictive Control for Competitive Autonomous Racing
论文作者
论文摘要
本文的目的是设计学习模型预测控制器（LMPC），该模型可以实时在预定义的赛道上进行竞争性比赛。本文解决了已经存在的单一代理公式中的两个主要缺点。以前，代理确定了本地最佳轨迹，但没有探索状态空间，这对于超越操作可能是必要的。另外，过去通过使用非凸线终端集来避免LMPC的障碍物，这增加了确定优化问题解决方案的复杂性。提出的用于多代理赛车的算法通过执行多个不同初始化的LMPC来探讨状态空间，从而产生更丰富的终端安全集。此外，开发了一种在终端集中选择状态的新方法，该方法可保留终端安全集的凸度，并允许采取次优状态。
The goal of this thesis is to design a learning model predictive controller (LMPC) that allows multiple agents to race competitively on a predefined race track in real-time. This thesis addresses two major shortcomings in the already existing single-agent formulation. Previously, the agent determines a locally optimal trajectory but does not explore the state space, which may be necessary for overtaking maneuvers. Additionally, obstacle avoidance for LMPC has been achieved in the past by using a non-convex terminal set, which increases the complexity for determining a solution to the optimization problem. The proposed algorithm for multi-agent racing explores the state space by executing the LMPC for multiple different initializations, which yields a richer terminal safe set. Furthermore, a new method for selecting states in the terminal set is developed, which keeps the convexity for the terminal safe set and allows for taking suboptimal states.
