Time-Optimal Planning for Quadrotor Waypoint Flight

Quadrotors are amongst the most agile flying robots. However, planning
time-optimal trajectories at the actuation limit through multiple
waypoints remains an open problem. This is crucial for applications such
as inspection, delivery, search and rescue, and drone racing. Early works
used polynomial trajectory formulations, which do not exploit the full
actuator potential due to their inherent smoothness.  Recent
works resorted to numerical optimization, but require waypoints to be
allocated as costs or constraints at specific discrete times. However,
this time-allocation is a priori unknown and renders previous works
incapable of producing truly time-optimal trajectories. To generate truly
time-optimal trajectories, we propose a solution to the time allocation
problem while exploiting the full quadrotor's actuator potential. We
achieve this by introducing a formulation of progress along the
trajectory, which enables the simultaneous optimization of the
time-allocation and the trajectory itself. We compare our method against
related approaches and validate it in real-world flights in one of the
world's largest motion-capture systems, where we outperform human
expert drone pilots in a drone-racing task.

四旋翼无人机（Quadrotors）是当前机动性最为出色的飞行机器人之一。然而，在执行器极限约束下规划经过多个航点（waypoints）的时间最优轨迹仍是一项尚未解决的公开难题。该问题在巡检、配送、搜救以及无人机竞速等应用场景中至关重要。早期研究采用多项式轨迹建模方法，由于其固有的平滑性，无法充分挖掘执行器的全部潜能。近期研究采用数值优化方法，但需将航点以代价项或约束条件的形式指定至特定离散时刻。然而，这类时间分配属于先验未知的设定，使得过往研究无法生成真正意义上的时间最优轨迹。为生成真正的时间最优轨迹，本文提出一种可同时求解时间分配问题并充分挖掘四旋翼执行器全部潜能的解决方案。该方案通过引入轨迹行进进度的建模表述，实现了时间分配与轨迹本身的联合优化。我们将本文所提方法与相关研究方案进行对比，并在全球规模最大的动作捕捉系统（motion-capture systems）之一中开展真实飞行实验以验证方法有效性，在无人机竞速任务中取得了超越人类专业无人机飞行员的成绩。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于四旋翼无人机航点飞行的时间最优规划问题，包含在大型运动捕捉系统中收集的飞行轨迹数据，涉及专业人类飞行员和提出的规划方法。数据集用于验证和比较时间最优轨迹生成技术，旨在提升无人机在检查、交付和竞速等应用中的性能。