二自由度云台的机器视觉控制

本文建立了基于机器视觉反馈的云台控制模型,并通过相邻帧间的目标位置预测来简化搜索算法,实现快速高效的视觉云台对运动目标的连续跟踪.在制定系统空间转角输入量的控制策略中,简化了控制模型并对产生的偏差量进行了分析.结果表明,简化后的控制算法能够实现快速反馈和像面调整,在25帧/秒时实现对运动目标稳健跟踪,且跟踪精度高于传统算法；在目标速度2 m/s、相机60帧/秒时,改进算法实现了对运动目标的连续跟踪.同时提出了立体视觉系统模型及其在云台控制中的应用,通过像面坐标获取空间位置信息以规避目标跟丢现象.

Controlling of 2-DOF PZT with Machine Vision

A vision-controlled model for two Degrees of Freedom (2-DOF) PZT is established, and through the analysis of the tracked object's location on the image planes of adjacent frames taken by the cameras, we realize a high efficiency and time-saved aim surveillance. Alternative functions are taken to depict the input angle that adjusts the PZT to track the moving object. It is found that the deviant brought in by the approximate model can be neglected compared to the accurate model. The location-feedback method improves the efficiency of aim-searching. The results demonstrate that with 25 frames per second, the camera is able to track the object with lower location deviant, compared with the original. Even when the object’s speed increases to 2 m/s, it could also stably track the object. A binocular vision model is proposed for the controlling system, and it can avoid the object-loss area during the tracking.