Abstract
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 this https URL
Abstract (translated)
虽然基于模型的控制器在自主无人机竞速中展现了卓越的表现，但它们的性能往往受限于对预计算参考轨迹的依赖。传统的路径跟踪方法需要动态可行且完整的状态参考，而轮廓控制则放松了这一要求，仅需几何路径作为参考，但仍存在依赖参考的问题。近期强化学习（RL）的研究表明，许多基于模型的控制器优化的是替代目标函数，例如路径跟踪，而非直接最大化通过门的速度这一主要竞速目标。受此启发，本文提出了一种无参考的方法，用于时间最优竞速，在Model Predictive Path Integral (MPPI) 方法中直接应用了从RL奖励塑形得出的门进度目标。由于MPPI方法基于采样特性，使其能够实时优化这一不连续且非可微的目标函数。此外，我们还建立了一个统一框架，利用MPPI系统地和公平地比较三种不同的目标函数，并采用一致的动力学模型和参数集：经典路径跟踪、轮廓控制以及所提出的门进度目标。在使用MPPI和传统梯度法求解时，我们评估了这三项目标函数的性能。实验结果表明，所提出的无参考方法达到了与基于参考的方法相当甚至更优的竞争性竞速表现。相关视频可在以下链接查看：[https URL]
URL
https://arxiv.org/abs/2509.14726
