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.

翻译：我们解决了在机载多旋翼无人机上实时规划经过多个航点的最小时间轨迹的挑战。先前的研究表明，实现真正的时间最优轨迹在计算上过于繁重，无法在敏捷飞行期间实现频繁的重新规划，尤其是在性能较低的飞行计算机上。我们的方法通过利用一个质点模型以及一种新颖的迭代推力分解算法克服了这一障碍，使得无人机能够利用其全部总推力，这是先前的质点模型方法无法实现的。该方法能够集成重力和阻力建模，显著减少了高速轨迹中的跟踪误差，这一点通过消融研究得到了证明。当与一种新的多航点优化算法结合时——该算法使用基于梯度的方法收敛到航点处的最优速度——所提出的方法能够在毫秒级时间内生成最小时间的多航点轨迹。我们以开源软件包形式提供的所提方法，在仿真和真实世界中均通过非线性模型预测控制进行了验证。在加速度高达3.5g且速度超过100公里/小时的情况下，所提方法生成的轨迹产生的跟踪误差与为完整多旋翼模型生成的轨迹相似甚至更小。