NMPP：杂乱环境中敏捷无人机飞行的非线性模型预测规划

中文摘要 AI 辅助

在杂乱环境中飞行四旋翼无人机，不仅需要基于感知到的障碍物规划出无碰撞的参考轨迹，而且该参考轨迹还需要在动力学上可行，并且处于车辆的作动极限之内，以便控制器能够精确跟踪。现有方法要么在凸走廊内优化平滑多项式，这限制了敏捷性，要么将障碍物视为与跟踪性能权衡的软成本。我们提出了一种非线性模型预测规划（NMPP），它将感知到的障碍物作为硬几何约束，并将全状态参考交给一个不考虑障碍物的SE(3)控制器。我们的规划器相比线性模型预测控制轨迹规划器，位置均方根误差（RMSE）降低了58-67%，相比多项式轨迹规划器，RMSE降低了41-70%。它还能以高达9.5米/秒的速度完成所有森林飞行且无碰撞，并在更激进的速度配置下实现了86%的飞行成功率，而最先进的规划器仅有26%的成功率。实际部署表明，在未知的杂乱环境中，以高达5.5米/秒的速度飞行时执行可靠。

英文摘要

Flying a quadrotor through a cluttered environment requires not only planning a collision-free reference trajectory based on perceived obstacles, but the reference also needs to be dynamically feasible and within the actuation limits of the vehicle, so that the controller can track it precisely. Existing methods either optimize a smooth polynomial inside a convex corridor, which limits agility, or treat obstacles as soft costs traded against tracking performance. We propose a Nonlinear Model Predictive Planning (NMPP) that imposes perceived obstacles as hard geometric constraints and hands a full-state reference to an obstacle-blind SE(3) controller. Our planner achieves a 58-67 % lower position RMSE than a linear Model Predictive Control trajectory planner and a 41-70 % lower RMSE than a polynomial trajectory planner. It also completes all forest flights with up to 9.5 m/s speed without collisions, and achieves 86 % flight success rate under a more aggressive speed profile where a state-of-the-art planner has only 26 % success rate. The real-world deployment showed reliable execution flying up to 5.5 m/s in an unknown cluttered environment.

发表机构

• Czech Technical University in Prague（捷克理工大学）

机构由 AI 辅助整理，请以论文原文为准。