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基于混合运动模型的相对状态估计

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
相对状态的精确估计通常依赖参考平台准确的角加速度，在航天场景由外力矩计算，而在日常环境中由于未知阻力普遍存在，准确的外力矩难以获取，角加速度一般由角速度差分近似，导致相对状态估计精度下降。对此，本文构建一种仅依赖惯性测量的相对运动模型，称为混合运动模型，其将恒定加速度模型与向量运动学结合，在独立于力矩和惯性状态的情况下，精确预测相对状态。此外，本文设计扩展卡尔曼滤波器(EKF)，将该运动模型与视觉相对观测结合，实现高精度相对状态估计，并以仿真和实际数据评估其性能。仿真实验从轨迹动态特性和相对观测丢失的角度对比该EKF和目前先进的方法，结果证明本文提出的方案具有较高的精度和稳定性。实际实验利用本方法实现虚拟现实应用中的六自由度手柄视觉跟踪，验证其具备先进的毫米级定位精度，演示链接见https://www。bili bili。com/video/BV1mv4y1d7iD/。
Relative state estimation based on hybrid kinematic model
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/.

relative state estimationmulti-robot collaborationvirtual realityextended Kalman filtersensor fusion

夏睿灿、裴海龙

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华南理工大学自动化科学与工程学院自主系统与网络控制教育部重点实验室

广东省无人机系统工程技术研究中心,广东广州 510640

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

国家重点研发计划项目航空科学基金项目国家自然科学基金重大科研仪器研制项目中国高校产学研创新基金新一代信息技术创新项目中央高校基本科研业务费专项资金

2023YFB470490020220056060001615278102022IT046

2024

10.7641/CTA.2024.30613

控制理论与应用
华南理工大学 中国科学院数学与系统科学研究院

控制理论与应用

CSTPCD北大核心
影响因子：1.076
ISSN：1000-8152
年,卷(期)：2024.41(7)