基于几何控制的采样式控制器，实现高速无人机精准跟踪与实时避障。
Geometric Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance
- 利用SE(3)几何控制生成部分轨迹样本，提升避障效率。
- 仿真中追踪误差与无避障控制器相当，避障能力超越现有方法。
- 支持真实飞行达17米/秒，避障速度达10米/秒，适合高速自主飞行场景。
本文提出几何模型预测路径积分（GMPPI），一种基于采样的控制器，可在保持高机动轨迹跟踪的同时实现在线障碍物避让。每轮迭代中，GMPPI生成大量候选轨迹并进行平均，形成无人机（UAV）应遵循的基准控制量。传统模型预测路径积分（MPPI）在轨迹追踪精度与避障性能间存在权衡：高噪声随机轨迹虽利于避障但影响追踪效果。为此，本文引入几何SE(3)控制生成部分轨迹样本，并设计针对无人机的代价函数，平衡追踪性能与避障需求。所有生成轨迹均投影至深度图像进行碰撞检测，据我们所知，这是首个将深度数据直接融入无人机MPPI循环的方法。仿真结果表明，GMPPI在追踪误差上达到无避障几何控制器水平，同时显著优于当前先进规划器和学习型控制器的避障能力。真实实验验证了其在最高17米/秒飞行速度下仍可实现有效避障，避障速度可达10米/秒。
原文摘要 · Abstract (English)
In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the controlled Unmanned Aerial Vehicle (UAV). Classical Model Predictive Path Integral (MPPI) faces a trade-off between tracking precision and obstacle avoidance; high-noise random rollouts are inefficient for tracking but necessary for collision avoidance. To this end, we propose leveraging geometric SE(3) control to generate a portion of GMPPI rollouts. To maximize their benefit, we introduce a UAV-tailored cost function balancing tracking performance with obstacle avoidance. All generated rollouts are projected onto depth images for collision avoidance, representing, to our knowledge, the first method utilizing depth data directly in a UAV MPPI loop. Simulations show GMPPI matches the tracking error of an obstacle-blind geometric controller while exceeding the avoidance capabilities of state-of-the-art planners and learning-based controllers. Real-world experiments demonstrate flight at speeds up to 17 m/s and obstacle avoidance up to 10 m/s.
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