实现无人机毫秒级最优路径规划，支持高速飞行时精准跟踪。
Real-time Planning of Minimum-time Trajectories for Agile UAV Flight
- 基于质点模型与迭代推力分解算法，充分释放无人机推力潜力。
- 在3.5g加速度、100km/h以上速度下，轨迹跟踪误差更小。
- 适合对实时性与飞行精度要求高的无人机自主导航场景。
本文解决多航点条件下机载多旋翼无人机的实时最小时间轨迹规划问题。以往方法因计算量过大，难以在高性能有限的飞行计算机上频繁重规划，尤其在高速敏捷飞行中。本文提出一种新型迭代推力分解算法，结合质点模型，使无人机能充分利用全部推力，突破了传统质点模型的限制。该方法集成重力与空气阻力建模，显著降低高速轨迹的跟踪误差（经消融实验验证）。配合新的多航点优化算法（基于梯度法求解航点最优速度），可在毫秒内生成最小时间多航点轨迹。所提方法以开源包形式提供，并通过非线性模型预测控制在仿真与真实场景中验证。在高达3.5g加速度、超过100 km/h的速度下，生成轨迹的跟踪误差与全多旋翼模型相当甚至更优。
原文摘要 · Abstract (English)
We address the challenge of real-time planning of minimum-time trajectories over multiple waypoints, onboard multirotor UAVs. Previous works demonstrated that achieving a truly time-optimal trajectory is computationally too demanding to enable frequent replanning during agile flight, especially on less powerful flight computers. Our approach overcomes this stumbling block by utilizing a point-mass model with a novel iterative thrust decomposition algorithm, enabling the UAV to use all of its collective thrust, something previous point-mass approaches could not achieve. The approach enables gravity and drag modeling integration, significantly reducing tracking errors in high-speed trajectories, which is proven through an ablation study. When combined with a new multi-waypoint optimization algorithm, which uses a gradient-based method to converge to optimal velocities in waypoints, the proposed method generates minimum-time multi-waypoint trajectories within milliseconds. The proposed approach, which we provide as open-source package, is validated both in simulation and in real-world, using Nonlinear Model Predictive Control. With accelerations of up to 3.5g and speeds over 100 km/h, trajectories generated by the proposed method yield similar or even smaller tracking errors than the trajectories generated for a full multirotor model.
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