MPCC++：面向具有安全约束的时间最优飞行的模型预测轮廓控制
MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints
AI总结：
本文提出MPCC++，通过赛道安全约束、残差气动动力学建模和TuRBO超参数调优，实现四旋翼时间最优竞速飞行，并在仿真与真实世界中保持100%避障成功率。
摘要：
由于在操控极限处遇到的有限控制权限，四旋翼飞行是一个极具挑战性的问题。模型预测轮廓控制（MPCC）已成为一种很有前景的、基于模型的方法，适用于无人机竞速等时间优化问题。然而，用于四旋翼竞速的标准MPCC表述将门的概念直接引入代价函数，形成了一个多目标优化，需要在最大化进度与精确跟踪路径之间持续权衡。本文介绍了三个关键组成部分，以增强用于无人机竞速的最先进MPCC方法。首先也是最重要的是，我们以赛道约束和终端集的形式提供安全保证。赛道约束被设计为一种空间约束，可防止撞门，同时仅允许在代价函数中进行时间优化。其次，我们在现有的第一性原理动力学中增加了一个残差项，该项捕捉直接从真实世界数据中学到的复杂空气动力学效应和推力。第三，我们使用Trust Region Bayesian Optimization（TuRBO）这一最先进的全局贝叶斯优化算法，基于单圈时间最小化的稀疏奖励来调节MPCC控制器的超参数。所提出方法在满足约束的同时，取得了与表现最佳的RL策略相近的单圈时间，并优于最佳的基于模型的控制器。在仿真和真实世界中，我们的方法均以100%的成功率持续防止撞门，同时将四旋翼推向其物理极限，达到超过80 km/h的速度。
英文摘要：
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 promising 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 state-of-the-art MPCC approach for drone racing. First and foremost, we provide safety guarantees in the form of a track constraint and terminal set. The track constraint 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 MPCC controller given a sparse reward based on lap time minimization. The proposed approach achieves similar lap times to the best-performing RL policy and outperforms the best model-based 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 limits reaching speeds of more than 80km/h.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
