Abstract
Time-optimal quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring Control (MPCC) has emerged as a leading model-based approach for time optimization problems such as drone racing. However, the standard MPCC formulation used in quadrotor racing introduces the notion of the gates directly in the cost function, creating a multi-objective optimization that continuously trades off between maximizing progress and tracking the path accurately. This paper introduces three key components that enhance the MPCC approach for drone racing. First and foremost, we provide safety guarantees in the form of a constraint and terminal set. The safety set is designed as a spatial constraint which prevents gate collisions while allowing for time-optimization only in the cost function. Second, we augment the existing first principles dynamics with a residual term that captures complex aerodynamic effects and thrust forces learned directly from real world data. Third, we use Trust Region Bayesian Optimization (TuRBO), a state of the art global Bayesian Optimization algorithm, to tune the hyperparameters of the MPC controller given a sparse reward based on lap time minimization. The proposed approach achieves similar lap times to the best state-of-the-art RL and outperforms the best time-optimal controller while satisfying constraints. In both simulation and real-world, our approach consistently prevents gate crashes with 100\% success rate, while pushing the quadrotor to its physical limit reaching speeds of more than 80km/h.
Abstract (translated)
时间最优的 quadrotor 飞行是一个极其具有挑战性的问题，因为在手柄的极限处遇到的控制权限非常有限。为了实现时间优化的无人机竞赛，模型预测控制（MPC）作为一种基于模型的方法已经成为了领先的模式。然而，在无人机竞赛中使用的标准 MPCC 形式在成本函数中引入了门的概念，导致目标函数连续地平衡在最大化进步和精确跟踪路径之间。本文介绍了三个关键组件，增强了在无人机竞赛中使用 MPC 的方法。首先，我们提供了安全保证，以约束和终止集的形式提供保障。安全集被设计为空间约束，在防止门碰撞的同时，仅允许在成本函数中进行时间优化。其次，我们通过残差项增加了现有的第一性原理动力学，并捕捉了从现实世界数据中获得的复杂空气动力学效应和推力。第三，我们使用了一种最先进的全球贝叶斯优化算法——Trust Region Bayesian Optimization (TuRBO)来根据基于 lap 时间最小化的稀疏奖励来调整 MPC 控制器的超参数。所提出的方法在类似 lap 时间内取得了与最佳状态下的 RL 相同的速度，并且在满足约束的情况下超越了最佳时间最优控制器。在仿真和现实世界里，我们的方法始终能够以 100% 的成功率防止门碰撞，并将无人机推向其物理极限，达到超过 80km/h 的速度。
URL
https://arxiv.org/abs/2403.17551
