论文标题
通过基于深度学习的3D姿势重建对水下软手的仿生评估
Biomimetic Evaluation of an Underwater Soft Hand Through Deep Learning-based 3D Pose Reconstruction
论文作者
论文摘要
软机器人的手在各种抓握应用中表现出巨大的希望。但是，机器人姿势的感应和重建将在设计和制造过程中导致限制。在这项工作中，我们提出了一种新颖的3D姿势重建方法，以使用实验视频分析双向软机器人手的握把运动。使用单相机 - 摩尔特式成像设备收集了从顶部，前，背部，左，右视图收集的图像。根据深度学习方法检测到软手指的坐标和方向信息。更快的RCNN模型用于检测指尖的位置，而U-NET模型则用于计算手指的侧边界。基于运动学，建立了相应的坐标和方向数据库。 3D姿势重建的结果表现出令人满意的性能和良好的准确性。使用功效系数方法，与人的手相比，通过分析了软机器人手的手指之间弯曲角和距离的手指贡献。结果表明，软机器人手在单向和双向握把中执行类似人类的运动。
Soft robotic hand shows considerable promise for various grasping applications. However, the sensing and reconstruction of the robot pose will cause limitation during the design and fabrication. In this work, we present a novel 3D pose reconstruction approach to analyze the grasping motion of a bidirectional soft robotic hand using experiment videos. The images from top, front, back, left, right view were collected using an one-camera-multiple-mirror imaging device. The coordinate and orientation information of soft fingers are detected based on deep learning methods. Faster RCNN model is used to detect the position of fingertips, while U-Net model is applied to calculate the side boundary of the fingers. Based on the kinematics, the corresponding coordinate and orientation databases are established. The 3D pose reconstructed result presents a satisfactory performance and good accuracy. Using efficacy coefficient method, the finger contribution of the bending angle and distance between fingers of soft robot hand is analyzed by compared with that of human hand. The results show that the soft robot hand perform a human-like motion in both single-direction and bidirectional grasping.
