Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.

翻译：敏捷四旋翼飞行依赖于快速规划并精确跟踪时间最优轨迹，这是其在野外场景应用中的关键技术。然而，基于完整四旋翼动力学（通常需数分钟甚至数小时）计算时间最优轨迹的运算负担，会限制其对动态环境的快速响应能力。此外，建模误差和外部扰动会导致实时跟踪过程中出现轨迹偏差。本文提出一种新型时间最优轨迹计算方法：通过固定航路点约束节点并在相邻航路点间采用独立采样间隔，从而显著加速轨迹规划。进一步地，规划路径采用时间自适应模型预测控制方案进行跟踪，其分配的跟踪时间可在线动态调整，从而增强跟踪精度与鲁棒性。我们通过仿真验证了该方法，并在动态航路点场景中通过实验验证了其在时间最优轨迹重规划与轨迹跟踪中的性能。