控制理论与应用2024，Vol.41Issue(7)：1207-1215,9.DOI:10.7641/CTA.2024.30613
基于混合运动模型的相对状态估计
Relative state estimation based on hybrid kinematic model
摘要
Abstract
The accurate estimation of relative state typically relies on the precise angular acceleration of a reference platform,which is calculated by applied torque in aerospace scenarios.However,since unknown resistances are common in daily environments,the precise applied torque is almost inaccessible and the angular acceleration is generally approximated through differential angular velocity,resulting in decreased accuracy of relative state estimation.For this issue,this paper introduces a novel relative motion model called the hybrid kinematic model that solely relies on inertial measurements.By incorporating the uniformly accelerated linear motion model with vector kinematics,it accurately predicts relative states independent of the torque and inertia states.Besides,an extended Kalman filter(EKF)is introduced to seamlessly integrate the kinematic model with visual relative observations,thereby achieving precise estimation of relative states.The effectiveness of this approach is assessed using both synthetic and real data.Simulation experiments compare the EKF with advanced methods in terms of trajectory dynamics and relative observations loss,demonstrating that the proposed solution offers superior accuracy and stability.In practical experiments,this method is utilized to implement visual tracking of a six-degree-of-freedom controller in virtual reality applications,validating its exceptional millimeter-level positioning accuracy.The demonstration can be found at https://www.bilibili.com/video/BV1mv4y1d7iD/.关键词
相对状态估计/多机器人协同/虚拟现实/扩展卡尔曼滤波器/传感器融合Key words
relative state estimation/multi-robot collaboration/virtual reality/extended Kalman filter/sensor fusion引用本文复制引用
夏睿灿,裴海龙..基于混合运动模型的相对状态估计[J].控制理论与应用,2024,41(7):1207-1215,9.基金项目
国家重点研发计划项目(2023YFB4704900),航空科学基金项目(20220056060001),国家自然科学基金重大科研仪器研制项目(61527810),中国高校产学研创新基金新一代信息技术创新项目(2022IT046),中央高校基本科研业务费专项资金资助.Supported by the National Key R&D Program of China(2023YFB4704900),the Aeronautical Science Foundation(20220056060001),the Scien-tific Instruments Development Program of NSFC(61527810),the New Generation of Information Technology Innovation Project of China University Innovation Fund(2022IT046)and the Fundamental Research Funds for the Central Universities. （2023YFB4704900）
