Local Gaussian Modifiers (LGMs)：面向机载计算的无人机动态轨迹生成
Local Gaussian Modifiers (LGMs): UAV dynamic trajectory generation for onboard computation
AI总结：
针对无人机机载计算能力有限、高速敏捷飞行中最优轨迹重算成本过高的问题，提出基于Local Gaussian Modifiers的快速轻量动态轨迹修正方法，仿真和实飞均验证了其有效性。
摘要：
敏捷自主无人机因在高速控制、状态估计或感知等领域带来的挑战，在研究中日益受到关注。当所有算法都在无人机机载端计算时，计算局限性使得敏捷且鲁棒的飞行任务变得更加困难。敏捷飞行中计算成本最高的任务之一是生成最优轨迹，该任务要解决四旋翼飞行器在一系列指定航点上规划最小时间轨迹的问题。当这些轨迹因环境变化或不确定性需要在线更新时，这种高计算成本可能导致无人机无法抵达目标航点，甚至在杂乱环境中坠毁。本文提出了一种快速轻量的动态轨迹修正方法，当因计算时间限制无法重新计算轨迹时，可利用Local Gaussian Modifiers（LGMs，局部高斯修正器）对计算密集型轨迹进行修改。\n 我们的方法在仿真中得到了验证，能够以最高16.0 m/s的速度穿越带有动态门的竞速赛道；同时也在真实飞行中得到验证，在完全自主的机载计算条件下，速度可达4.0 m/s。
英文摘要：
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.
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