发表机构
School of Robotics and Automation, Nanjing University（南京大学机器人与自动化学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机在狭窄空间自主飞行因干扰和空间受限面临的挑战，提出干扰感知规划与控制框架DAPCF，通过双环观测器估计干扰、引入干扰风险函数及设计MDNMPC，使四旋翼能穿越狭窄隧道，性能优于人类飞行员。
AI 中文摘要
由于强烈的空气动力学干扰和有限的飞行空间，无人机在狭窄空间的自主飞行仍然具有挑战性。现有方法主要在控制层面解决空气动力学干扰，而运动规划通常依赖几何约束和固定速度限制，在受限环境中导致保守或不安全行为。本文提出一种干扰感知规划与控制框架（DAPCF），将在线干扰估计集成到狭窄空间四旋翼飞行的规划控制回路中。首先，双环观测器基于里程计和电机速度测量实时估计六自由度干扰力和扭矩。然后，引入干扰风险函数，根据干扰估计自适应调节规划器的参考速度，在干扰超过阈值时降低速度，在低干扰条件下恢复速度。最后，设计基于电机动力学的带干扰补偿的非线性模型预测控制器（MDNMPC），以确保在扰动条件下的鲁棒轨迹跟踪。实验表明，对角线长度为0.39米的四旋翼可以穿越窄至0.6米的直、斜坡和弯曲隧道，在成功率和飞行效率方面均优于人类飞行员。
英文摘要
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.
