用摄影测量法无损校准六足平台,提升定位精度。
Non-Invasive Calibration Of A Stewart Platform By Photogrammetry
- 基于相机和算法捕捉运动平台位姿,无需改装硬件。
- 三种补偿策略使平台位姿误差显著降低。
- 适合需要高精度校准的机器人或精密仪器研发者。
精确校准六足平台对其精准高效运行至关重要。然而,传统基于正向运动学的校准方法因存在多个可行与不可行解而面临挑战,且六个执行器路径间的复杂运动关系使得建立直接高效的校准方法极为困难。本文提出一种基于Denavit-Hartenberg参数的正向运动学校准新方法,并在实验室自研的Tiger 66.1六足平台上进行实验验证。该系统在搭建完成后首次投入使用。通过高分辨率数字相机和现成软件捕获运动平台中心的多角度图像,实现三维空间中位置与姿态的非侵入式测量。将目标位姿与实际位姿对比,利用最小二乘法估计误差并计算预测位姿。三种补偿策略均显著提升了平台位姿精度,表明仍有优化空间。
原文摘要 · Abstract (English)
Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg convention and used the Stewart platform Tiger 66.1 developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per Least Square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform's center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The Target poses and Actual poses are then compared, and the error compensations are estimated using the Least-Squared methods to calculate the Predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements.
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