Agile autonomous drones are becoming increasingly popular in research due to the challenges they represent in fields like control, state estimation, or perception at high speeds. When all algorithms are computed onboard the uav, the computational limitations make the task of agile and robust flight even more difficult. One of the most computationally expensive tasks in agile flight is the generation of optimal trajectories that tackles the problem of planning a minimum time trajectory for a quadrotor over a sequence of specified waypoints. When these trajectories must be updated online due to changes in the environment or uncertainties, this high computational cost can leverage to not reach the desired waypoints or even crash in cluttered environments. In this paper, a fast lightweight dynamic trajectory modification approach is presented to allow modifying computational heavy trajectories using Local Gaussian Modifiers (LGMs), when recalculating a trajectory is not possible due to the time of computation. Our approach was validated in simulation, being able to pass through a race circuit with dynamic gates with top speeds up to 16.0 m/s, and was also validated in real flight reaching speeds up to 4.0 m/s in a fully autonomous onboard computing condition.

翻译：敏捷自主无人机因其在高速控制、状态估计与感知等领域带来的挑战，正日益成为研究热点。当所有算法均在无人机机载计算单元上运行时，计算资源限制使得实现敏捷稳健飞行的任务更为艰巨。敏捷飞行中计算成本最高的任务之一是最优轨迹生成——为解决四旋翼飞行器在指定航点序列中规划最小时间轨迹的问题。当环境变化或存在不确定性需要在线更新轨迹时，这种高计算成本可能导致无法抵达目标航点，甚至在复杂环境中发生碰撞。本文提出一种快速轻量级的动态轨迹修正方法，允许在因计算时间限制无法重新生成轨迹时，利用局部高斯修正器（LGMs）对计算量大的轨迹进行修正。该方法经过仿真验证：在动态闸门赛道中以最高16.0米/秒速度通过；并在全自主机载计算条件下完成真实飞行测试，最高速度达4.0米/秒。