Robotic grasping of arbitrary objects even in completely known environments still remains a challenging problem. Most previously developed algorithms had focused on fingertip grasp, failing to solve the problem even for fully actuated hands/grippers during adaptive/wrapping type of grasps, where each finger makes contact with object at several points. Kinematic closed form solutions are not possible for such an articulated finger which simultaneously reaches several given goal points. This paper, presents a framework for computing best grasp for an underactuated robotic gripper, based on a novel object slicing method. The proposed method quickly find contacts using an object slicing technique and use grasp quality measure to find the best grasp from a pool of grasps. To validate the proposed method, implementation has been done on twenty-four household objects and toys using a two finger underactuated robot gripper. Unlike the many other existing approaches, the proposed approach has several advantages: it can handle objects with complex shapes and sizes; it does not require simplifying the objects into primitive geometric shape; Most importantly, it can be applied on point clouds taken using depth sensor; it takes into account gripper kinematic constraints and generates feasible grasps for both adaptive/enveloping and fingertip types of grasps.
A Dexterous and Compliant Gripper With Soft Hydraulic Actuation for Microgravity Manipulation
Aerospace Engineering Program, University of California, Berkeley（加州大学伯克利分校航空航天工程系）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）
Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators
Institute for Robotics Research（机器人研究所）；Centre for Robotics Research（机器人研究中心）；Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究所，科技研究局（A*STAR））
Tactile Model O: Fabrication and testing of a 3d-printed, three-fingered tactile robot hand
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
为机器人手带来触觉将允许更有效的抓握，以及类似人类触摸的广泛好处。在这里，我们提出了一个3D打印的三指触觉机器人手，包括一个定制的OpenHand Model O，用于在每个手指的远端指骨中容纳TacTip柔软的仿生触觉传感器。我们期望将这种欠驱动手的抓取能力与复杂的触觉感知相结合，将导致一个有效的机器人手研究平台-触觉模型O（T-MO）。该设计使用了三个JeVois机器视觉系统，每个系统都包括一个位于触觉指尖的微型摄像头和一个位于手底部的处理模块。为了评估T-MO的能力，我们使用YCB对象集上的抓取器评估基准对其抓取性能进行基准测试。触觉感知的能力进行评估，通过执行触觉对象分类的26个对象，并预测是否把握将成功地解除每个对象。结果与最新技术水平一致，利用了应用于触觉图像输出的深度学习的进步。总的来说，这项工作表明，T-MO是一个有效的平台，机器人手的研究，我们希望它打开了一系列的应用在自主物体处理。补充视频：https://youtu.be/RTcCpgffCrQ。
Hoecken-D Hand: A Novel Robotic Hand for Linear Parallel Pinching and Self-Adaptive Grasping
Computer Science and Technology, Beijing Institute of Technology, China（计算机科学与技术，北京理工大学，中国）；Laboratory of Robotics, X-Institute（机器人实验室，X研究所）；Dept. of Mechanical Engineering, Tsinghua University, Beijing, China（机械工程系，清华大学，北京，中国）
