用安全滤波器让无人机在干扰下也能避障跟踪，无需提前考虑安全约束。
Safe Reinforcement Learning Filter for Multicopter Collision-Free Tracking under disturbances
- 引入鲁棒控制屏障函数，动态调整增益确保状态安全。
- 通过二次规划求解，保证在输入饱和下仍输出安全控制信号。
- 仿真与实测均验证其在干扰下避障跟踪的可靠性。
本文提出一种安全强化学习滤波器（SRLF），用于多旋翼在输入扰动下实现无碰撞轨迹跟踪。提出一种新型鲁棒控制屏障函数（RCBF）及其分析方法，在跟踪过程中避免与未知扰动导致的碰撞。为确保系统状态始终处于安全集内，将RCBF增益融入控制动作设计。引入安全滤波器，将不安全的强化学习（RL）控制输入转换为安全输出，使RL训练可脱离显式安全约束进行。SRLF通过求解包含RCBF前向不变性与输入饱和约束的二次规划（QP）问题，获得严格保障的安全控制动作。多旋翼的仿真与真实实验均证明SRLF在输入扰动与饱和条件下具有优异的无碰撞跟踪性能。
原文摘要 · Abstract (English)
This paper proposes a safe reinforcement learning filter (SRLF) to realize multicopter collision-free trajectory tracking with input disturbance. A novel robust control barrier function (RCBF) with its analysis techniques is introduced to avoid collisions with unknown disturbances during tracking. To ensure the system state remains within the safe set, the RCBF gain is designed in control action. A safety filter is introduced to transform unsafe reinforcement learning (RL) control inputs into safe ones, allowing RL training to proceed without explicitly considering safety constraints. The SRLF obtains rigorous guaranteed safe control action by solving a quadratic programming (QP) problem that incorporates forward invariance of RCBF and input saturation constraints. Both simulation and real-world experiments on multicopters demonstrate the effectiveness and excellent performance of SRLF in achieving collision-free tracking under input disturbances and saturation.
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