Abstract
Agile quadrotor flight pushes the limits of control, actuation, and onboard perception. While time-optimal trajectory planning has been extensively studied, existing approaches typically neglect the tight coupling between vehicle dynamics, environmental geometry, and the visual requirements of onboard state estimation. As a result, trajectories that are dynamically feasible may fail in closed-loop execution due to degraded visual quality. This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors that explicitly incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations. The proposed formulation solves minimum-time lap trajectories for arbitrary racetracks with diverse gate shapes and orientations, while remaining numerically robust and computationally efficient. We derive an information-theoretic position uncertainty metric to quantify visual state-estimation quality and integrate it into the planner through three perception objectives: position uncertainty minimization, sequential field-of-view constraints, and look-ahead alignment. This enables systematic exploration of the trade-offs between speed and perceptual reliability. To accurately track the resulting perception-aware trajectories, we develop a model predictive contouring tracking controller that separates lateral and progress errors. Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100% on a challenging Split-S course. The proposed system provides a scalable benchmark for studying the fundamental limits of perception-aware, time-optimal autonomous flight.
Abstract (translated)
本文介绍了基于视觉的四旋翼无人机时间最优轨迹优化框架，该框架明确地将感知约束与完整的非线性动力学、电机操作限制、气动效应、摄像机视场约束以及凸几何门表示结合在一起。现有的方法通常忽略了车辆动态特性、环境几何形状和车载状态估计所需的视觉需求之间的紧密联系，导致虽然某些轨迹在动力学上可行，但在闭环执行中由于视觉质量下降而失败。 该提出的框架能够为具有各种门形状和方向的任意赛道生成最小时间圈迹，同时保持数值稳健性和计算效率。我们引入了一个信息论位置不确定性度量来量化基于视觉的状态估计质量，并通过三个感知目标将其集成到规划器中：减少位置不确定性、顺序视场约束以及前瞻对齐。这使系统能够有条不紊地探索速度与感知可靠性的权衡。 为了准确跟踪这些感知敏感轨迹，我们开发了一种模型预测轮廓追踪控制器，该控制器将横向和进度误差分开处理。实验表明，在具有挑战性的“Split-S”赛道上，闭环成功几率从55%提高到了100%，同时实现了高达9.8米/秒的现实世界飞行速度，并且平均跟踪误差为0.07米。 所提出的系统为研究感知意识的时间最优自主飞行的基本限制提供了可扩展的基准。
URL
https://arxiv.org/abs/2603.04305
