自动化学报2026，Vol.52Issue(2)：309-321,13.DOI:10.16383/j.aas.c250535
基于传感重构的高可靠无人飞行器自动防撞策略
High-reliability Automatic Collision Avoidance Strategy for Unmanned Aerial Vehicles Based on Sensing Reconstruction
摘要
Abstract
Addressing the safety flight requirements of complex airspace by unmanned aerial vehicles in the con-text of low-altitude economy development,this paper systematically considers the failure issue of atmospheric sensors under strong wind interference and proposes a high-reliability automatic collision avoidance strategy based on sensing reconstruction.Firstly,an aircraft dynamics model incorporating turbulence disturbances is established,and an adaptive cubature Kalman filter is employed to fuse navigation measurements and control signals,achieving robust online reconstruction of states such as true airspeed and airflow angles.Secondly,to address model mis-match and noise disturbances during the escape phase,an intelligent learning-based adaptive control law is de-signed to compensate for state estimation errors,enabling stable tracking of escape maneuver commands.Finally,a dynamic collision envelope driven by the filter covariance is constructed,and trajectory prediction uncertainty is quantified by integrating the control system model to complete terrain collision detection.This facilitates the gener-ation of optimal obstacle avoidance commands by evaluating multiple escape trajectories.Simulation results show that accurate airflow angle reconstruction and robust collision warning and recovery control are achieved under gust and severe turbulence conditions.The related techniques can provide a reliable solution for the design of collision avoidance systems in low-altitude unmanned aerial vehicles.关键词
自动防撞/大气数据系统/姿态控制/威胁评估/风干扰Key words
automatic collision avoidance/air data system/attitude control/threat assessment/wind disturbance引用本文复制引用
李睿,许斌,阎振鑫,杨林..基于传感重构的高可靠无人飞行器自动防撞策略[J].自动化学报,2026,52(2):309-321,13.基金项目
国家自然科学基金(61933010),西北工业大学博士论文创新基金(CX2025017)资助Supported by National Natural Science Foundation of China(61933010)and Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(CX2025017) （61933010）
