Details of Research Outputs

为克服机器人视觉伺服系统对位姿估算及标定精度的依赖性,结合前期特征识别和后期视觉伺服控制,提出基于冗余特征的机器人视觉伺服控制方法.首先,针对图像处理计算密集的问题,从加快特征提取运算速度考虑,研究了矢量数据的递归贪婪压缩算法;其次,从提高图像空间测量精度考虑,研究了基于向量正交性的亚像素特征提取方法,并结合合作目标形状,给出基于多边形形状拟合的目标识别实验性准则;最后,基于图像视觉伺服理论和任务函数方法,直接以具有亚像素级的冗余图像特征作为反馈信息,建立了机器人视觉伺服控制模型,并进行了视觉伺服验证试验.理论分析和实验结果表明,本文提出的视觉伺服控制方法能够在复杂的环境下快速稳定地提取伺服特征,并对标定误差和深度估计误差具有一定的鲁棒性. 

To overcome the dependency of a robot visual servoing system on calibration accuracy and pose estimation, a redundant featurebased robot visual servoing control method was proposed via fusing object feature recognition and visual servoing in different stages respectively. Firstly, to address the issue of largescale computation on image processing, a recursive greedy data compression algorithm based on curve vector was designed to speed up the feature extraction process. Secondly, to improve the measurement precision of feature in image space, a subpixel feature extraction method was studied based on the principle of vector orthogonallity. Furthermore, experimental rule of object recognition was proposed based on cooperative object shape and polygon shape fitting. Finally, based on the theory of image visual servoing and the method of task function, redundant visual features in subpixels were selected directly as feedback signals to build controlling model of robot visual servoing. The theoretical analysis and experimental results show that the servoing features can be extracted quickly and stably in complex environments, and the proposed method was robust to the calibration error and depth estimation error.