基于图优化的通信受限环境下协同导航方法  

Method for cooperative navigation in constrained environment based on graph optimization

作　　者：牛皓飞 蔡庆中 李健[1] 杨功流[1] NIU Haofei;CAI Qingzhong;LI Jian;YANG Gongliu(School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100191

出　　处：《航空学报》2023年第11期222-234,共13页Acta Aeronautica et Astronautica Sinica

基　　金：国家自然科学基金(61803015)。

摘　　要：近年来智能无人集群系统受到广泛关注,集群中多智能体节点的协同导航是实现复杂协同控制的一个关键问题。针对在复杂遮挡环境下可能存在的观测信息不同步、不连续等问题,研究基于图优化算法的分布式协同导航方法。以超宽带(UWB)测距传感器提供的节点间相对测距信息与节点自身基于惯性传感器的航位推算信息构成的状态约束建立因子图模型,利用图优化算法全局优化的特点解决传统滤波方法无法适应观测信息不同步的问题;通过自适应动态变化的分布式拓扑结构,可充分利用节点间的观测信息解决复杂环境下锚点观测不足的问题;通过对陀螺零偏的实时估计提高个体在短时间内完全无外部观测状态下的精度保持能力。数学仿真与实验结果表明:在复杂遮挡环境下,所提方法具备处理动态增删、时间异步的观测信息的能力,并可以对陀螺零偏进行实时估计与补偿,使短时通信拒止环境下单节点的位置精度保持能力提升61%,使集群在复杂通信受限环境的综合定位精度提升1倍。In recent years,the intelligent unmanned cluster system has attracted extensive attention.The cooperative navigation of multi-agent nodes in the cluster is a key problem to realize complex cooperative control.To overcome the possible problems of asynchronous and discontinuous measurement in the complex occlusion environment,a distrib⁃uted collaborative navigation method is proposed based on graph optimization algorithm.The factor graph model is es⁃tablished based on the state constraints composed of the relative ranging information between nodes provided by(Ul⁃tra Wide Band,UWB)sensors and the dead reckoning information of nodes based on inertial sensors.The global op⁃timization characteristics of graph optimization algorithm are used to solve the problem that the traditional filtering meth⁃ods cannot adapt to non synchronization of measurement.Through the adaptive dynamic distributed topology,the measurement between nodes can be fully used to solve the problem of insufficient measurement of anchor points in the complex environment.Based on the real-time estimation of gyro bias,the individual vehicle’s ability to maintain measurement accuracy without external measurement in a short time is improved.The mathematical simulation and experimental results show that in the complex occlusion environment,the proposed method has the ability to process the measurement of dynamic addition and deletion and time asynchrony,and can estimate and compensate the gyro bias in real time.It can improve the positioning accuracy retention ability of a single node in the short-term communica⁃tion rejection environment by 61%,and the comprehensive positioning accuracy of the cluster in the complex environ⁃ment of limited communication is doubled.

关 键 词：协同导航 无人集群 图优化 因子图 多智能体 

分 类 号：V448.21[航空宇航科学与技术—飞行器设计]