Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc. Perception is considered one of the major challenges in DOM due to the complex dynamics and high degree of freedom of deformable objects. In this paper, we develop a novel image-processing algorithm based on Gabor filters to extract useful features from cloth, and based on this, devise a strategy for cloth flattening tasks. We evaluate the overall framework experimentally, and compare it with three human operators. The results show that our algorithm can determine the direction of wrinkles on the cloth accurately in the simulation as well as the real robot experiments. Besides, the robot executing the flattening tasks using the dewrinkling strategy given by our algorithm achieves satisfying performance compared to other baseline methods. The experiment video is available on https://sites.google.com/view/robotic-fabric-flattening/home
翻译:变形物体操纵( DOM) 是一个重要的研究领域, 因为它有助于自动布布处理、 电缆路由、 外科手术等实际任务。 由于变形物体的复杂动态和高度自由性, 觉悟被认为是DOM面临的主要挑战之一。 在本文中, 我们根据加博过滤器开发了一种新的图像处理算法, 从布料中提取有用的特征, 并以此为基础, 设计了布板平整任务的战略。 我们实验性地评估了整个框架, 并将其与三个人类操作员进行了比较。 结果表明, 我们的算法可以准确确定模拟和真正的机器人实验中布料皱纹的方向。 此外, 使用我们算法给出的淡化战略执行平坦任务的机器人能够与其他基线方法相比取得令人满意的性能。 实验视频可在 https://sites.gogle. com/view/robtoy- fabric- flatic- fallteninging/ home 上查阅/ home 上查阅。</s>