让无人机在狭窄空间更安全高效飞行，靠实时感知风力干扰动态调整速度和控制。
Disturbance-Aware Flight for Aerial Robots in Narrow Space
- 通过双环观测器实时估算六自由度干扰力与力矩。
- 干扰过大时自动降速，低干扰时恢复速度，提升飞行安全性。
- 结合电机动力学的模型预测控制，抗扰能力更强，适合复杂窄道飞行。
无人机在狭窄空间自主飞行仍面临强气动干扰和有限飞行空间的挑战。现有方法多在控制层面处理干扰，运动规划则依赖几何约束和固定速度限制，导致在狭小环境中行为保守或不安全。本文提出一种干扰感知的规划与控制框架（DAPCF），将在线干扰估计融入规划-控制闭环，用于四旋翼在窄空间飞行。首先，基于里程计和电机转速测量，双环观测器实时估计六自由度干扰力与力矩。其次，引入干扰风险函数，根据干扰估计值自适应调节规划器参考速度：当干扰超过阈值时降低速度，低干扰时恢复。最后，设计基于电机动力学的非线性模型预测控制器（MDNMPC），具备干扰补偿能力，确保扰动条件下轨迹跟踪稳定。实验表明，对边长0.39米的四旋翼，可穿越宽度仅0.6米的直、斜、弯隧道，成功率与飞行效率均优于人类飞行员。
原文摘要 · Abstract (English)
Autonomous flight of aerial robots in narrow space remains challenging due to strong aerodynamic disturbances and limited flying space. Existing approaches mainly address aerodynamic disturbances at the control level, while motion planning typically relies on geometric constraints and fixed speed limits, leading to conservative or unsafe behaviors in confined environments. This paper presents a disturbance-aware planning and control framework (DAPCF) that integrates online disturbance estimation into the planning-control loop for quadrotor flight in narrow space. First, the dual-loop observers estimate 6-degree-of-freedom disturbance forces and torques in real time based on odometry and motor speed measurements. Then, a disturbance risk function is introduced that adaptively modulates the reference speed of the planner based on disturbance estimation, reducing velocity when disturbances exceed a threshold and restoring it under low-disturbance conditions. Finally, a motor-dynamics-based nonlinear model predictive controller (MDNMPC) with disturbance compensation is designed to ensure robust trajectory tracking under perturbed conditions. Experiments demonstrate that a quadrotor with a diagonal length of 0.39~m can traverse straight, sloped, and curved tunnels as narrow as 0.6~m, outperforming human pilots in both success rate and flight efficiency.
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