用多陀螺仪融合+智能补偿，精准追踪柔性机械臂运动
AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion
- 将柔性臂分段建模，通过低成本陀螺仪测量关节角
- 粒子群优化滤波参数，使位置误差低于0.00041米
- 用神经网络修正残差，适合高精度机器人控制场景
本文提出一种基于多惯性测量单元（IMU）的新型框架，用于估计垂直运动中柔性机械臂的位置与姿态，利用真实数据进行优化和标定。柔性臂被建模为一系列刚性段，关节角度由低成本IMU采集的加速度计与陀螺仪数据估算。采用互补滤波融合数据，其参数通过粒子群优化（PSO）调整以抑制噪声与延迟。为进一步提升精度，使用径向基函数神经网络（RBFNN）对位置和姿态的残差误差进行补偿。实验验证了该智能多IMU运动估计方法的有效性，$y$、$z$和$θ$方向的均方根误差（RMSE）分别达到0.00021~m、0.00041~m和0.00024~rad。
原文摘要 · Abstract (English)
This paper presents a novel framework for estimating the position and orientation of flexible manipulators undergoing vertical motion using multiple inertial measurement units (IMUs), optimized and calibrated with ground truth data. The flexible links are modeled as a series of rigid segments, with joint angles estimated from accelerometer and gyroscope measurements acquired by cost-effective IMUs. A complementary filter is employed to fuse the measurements, with its parameters optimized through particle swarm optimization (PSO) to mitigate noise and delay. To further improve estimation accuracy, residual errors in position and orientation are compensated using radial basis function neural networks (RBFNN). Experimental results validate the effectiveness of the proposed intelligent multi-IMU kinematic estimation method, achieving root mean square errors (RMSE) of 0.00021~m, 0.00041~m, and 0.00024~rad for $y$, $z$, and $θ$, respectively.
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