论文标题
自主赛车中战略运动计划的层次结构方法
A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing
论文作者
论文摘要
我们提出了一种安全轨迹计划的方法，其中在模拟环境中学习了与自主赛车相关的战略任务。 表示为神经网络的高级策略，输出奖励规范，该规范用于参数非线性模型预测控制器（NMPC）的成本函数。通过在NLP中包括约束和车辆运动学，我们可以保证与使用模型相关的安全且可行的轨迹。与经典的增强学习（RL）相比，我们的方法将探索限制在安全轨迹上，始于良好的先前性能，并产生可以传递给最低级别控制器的完整轨迹。我们没有解决这项工作中最低级别的控制器，而是对可行轨迹的完美跟踪。我们在模拟赛车任务上显示了包括高级决策的模拟赛车任务的卓越性能。该车辆学会了有效地超过车辆较慢的车辆，并避免通过阻止更快的车辆而超越。
We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample-efficient within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the cost function of a parametric nonlinear model predictive controller (NMPC). By including constraints and vehicle kinematics in the NLP, we are able to guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning (RL), our approach restricts the exploration to safe trajectories, starts with a good prior performance and yields full trajectories that can be passed to a tracking lowest-level controller. We do not address the lowest-level controller in this work and assume perfect tracking of feasible trajectories. We show the superior performance of our algorithm on simulated racing tasks that include high-level decision making. The vehicle learns to efficiently overtake slower vehicles and to avoid getting overtaken by blocking faster vehicles.
