Research on the position control of double-pass schlieren component based on visual feedback
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摘要: 在高超声速低密度风洞试验中,采用传统机械方法调试双光程纹影系统,无法确保球面镜机构工作位置的精细定位,光路两次穿过流场后无法完全重合。针对上述问题,研发了基于视觉反馈的双光程纹影部件位置控制系统。采用绝对式编码器指令控制交流伺服电机,对球面镜机构的工作位置进行定位控制;引入机器视觉系统,结合视觉信息反馈技术,对纹影图像质量进行研判,根据研判结果确定是否对球面镜的俯仰、左右偏转进行调节。结果表明:采用基于视觉反馈的双光程纹影部件位置控制系统,实现了双光程纹影球面镜机构的自动定位闭环控制,确保光路两次穿过流场后尽量重合,消除了模型流场成像时的重影;与传统方法相比,流场图像的清晰度提高了约2.2倍。Abstract: The traditional mechanical method of debugging the double-pass schlieren system exhibits the problems that the fine positioning of the working position of the spherical mirror mechanism cannot be ensured, and the optical paths cannot be completely coincided after passing through the flow field twice in the experimental application in the hypersonic low density wind tunnel. Here, a novel double-pass schlieren system based on visual feedback was developed. The system via absolute encoder instruction control the AC servo motor to adjust the position of the spherical mirror mechanism. Moreover, the pitch and left-right deflection of the spherical mirror can be adjusted by the schlieren image quality evaluation results provided by the machine vision system(visual information feedback). The position control system of double-pass schlieren parts based on visual feedback realizes the automatic positioning closed-loop control of the double-pass schlieren spherical mirror mechanism, and ensures that the light paths overlap as much as possible after passing through the flow field twice to eliminate ghosting during imaging of the model flow field(the definition of the flow field image is improved by 2.2 times compared with that obtained by the traditional method).
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Key words:
- wind tunnel experiment /
- double-pass schlieren /
- positioning control /
- visual feedback /
- image clarity
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输入:CCD采集RGB三通道图像 FRe():二值化初步提取飞行器区域 FTa():提取目标检测区域 FCo():计算评价函数值 for p=1∶1∶i;q=1∶1∶j; FCo+ =hpq return FCo; 输出:清晰度评价函数Count值 表 1 试验计算图像清晰度参数表
Table 1. Table of experimentally calculated image sharpness parameters
机械调试整体图像 视觉反馈整体图像 机械调试局部图像 视觉反馈局部图像 能量梯度函数
Value值21079058 67152401 7352238 36270757 自适应算法
Count值6214 611 6008 593 能量梯度函数
算法时间14.0634 s 14.1419 s 12.7405 s 12.8679 s 自适应算法
算法时间243.1 ms 240.5 ms 187.6 ms 192.1 ms -
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