通过分层控制分解，实现电机饱和下的安全无人机轨迹规划。
Quad-LCD: Layered Control Decomposition Enables Actuator-Feasible Quadrotor Trajectory Planning
- 将控制分解为可执行层级，解决电机饱和导致的失控问题。
- 仿真中激进机动下事故率降低约49%。
- 代码开源，适合硬件平台快速部署的开发者使用。
本文针对四旋翼系统在电机饱和情况下的数据驱动轨迹生成问题提出改进方法。电机饱和会导致飞行器失控漂移并引发坠毁。为此，我们采用控制分解策略，并从包含低、中、高成本参考轨迹的仿真数据中学习跟踪惩罚。该方法在仿真中对激进机动的崩溃率相较基线降低约49%。在Crazyflie硬件平台上，实验验证了其可行性，实现了成功飞行。鉴于数据驱动方法在四旋翼规划中的日益重要，我们公开了轻量级代码，提供易用的硬件平台抽象接口。
原文摘要 · Abstract (English)
In this work, we specialize contributions from prior work on data-driven trajectory generation for a quadrotor system with motor saturation constraints. When motors saturate in quadrotor systems, there is an ``uncontrolled drift" of the vehicle that results in a crash. To tackle saturation, we apply a control decomposition and learn a tracking penalty from simulation data consisting of low, medium and high-cost reference trajectories. Our approach reduces crash rates by around $49\%$ compared to baselines on aggressive maneuvers in simulation. On the Crazyflie hardware platform, we demonstrate feasibility through experiments that lead to successful flights. Motivated by the growing interest in data-driven methods to quadrotor planning, we provide open-source lightweight code with an easy-to-use abstraction of hardware platforms.
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