重新思考敏捷无人机竞速中的参考轨迹：一种基于MPPI的统一无参考模型控制器
Rethinking Reference Trajectories in Agile Drone Racing: A Unified Reference-Free Model-Based Controller via MPPI
AI总结：
本文提出一种基于MPPI的无参考无人机竞速控制方法，将门禁进度目标直接纳入优化，并与轨迹跟踪和轮廓控制在统一框架下比较，证明其性能达到或超过基于参考的方法。
摘要：
尽管基于模型的控制器在自主无人机竞速中已展现出卓越性能，但其表现往往受限于对预先计算参考轨迹的依赖。传统方法（如轨迹跟踪）要求具备动态可行的全状态参考，而轮廓控制虽将该要求放宽为几何路径，但仍需要参考。强化学习（RL）的最新进展表明，许多基于模型的控制器优化的是替代目标（如轨迹跟踪），而非直接最大化穿越门禁的进度这一首要竞速目标。受这些发现启发，本文引入一种无参考方法，通过将源自RL奖励塑形的门禁进度目标直接纳入模型预测路径积分（MPPI）公式中，实现时间最优竞速。MPPI基于采样的特性使其能够独特地实时优化不连续且不可微的目标。我们还建立了一个统一框架，利用MPPI在一致的动力学模型和参数集下，系统且公平地比较三种不同的目标函数：经典轨迹跟踪、轮廓控制以及所提出的门禁进度目标。我们比较了这三种目标分别通过MPPI和传统基于梯度的求解器求解时的性能。结果表明，所提出的无参考方法实现了具有竞争力的竞速性能，达到或超过了基于参考的方法。视频见 https://zhaofangguo.github.io/racing_mppi/
英文摘要：
While model-based controllers have demonstrated remarkable performance in autonomous drone racing, their performance is often constrained by the reliance on pre-computed reference trajectories. Conventional approaches, such as trajectory tracking, demand a dynamically feasible, full-state reference, whereas contouring control relaxes this requirement to a geometric path but still necessitates a reference. Recent advancements in reinforcement learning (RL) have revealed that many model-based controllers optimize surrogate objectives, such as trajectory tracking, rather than the primary racing goal of directly maximizing progress through gates. Inspired by these findings, this work introduces a reference-free method for time-optimal racing by incorporating this gate progress objective, derived from RL reward shaping, directly into the Model Predictive Path Integral (MPPI) formulation. The sampling-based nature of MPPI makes it uniquely capable of optimizing the discontinuous and non-differentiable objective in real-time. We also establish a unified framework that leverages MPPI to systematically and fairly compare three distinct objective functions with a consistent dynamics model and parameter set: classical trajectory tracking, contouring control, and the proposed gate progress objective. We compare the performance of these three objectives when solved via both MPPI and a traditional gradient-based solver. Our results demonstrate that the proposed reference-free approach achieves competitive racing performance, rivaling or exceeding reference-based methods. Videos are available at https://zhaofangguo.github.io/racing_mppi/
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。
