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How to manipulate an object robustly with only one actuator (An application of caging)

机译:如何只用一个执行器(持续的持续函数)鲁棒地操纵一个物体

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Caging can offer robustness to uncertainties in grasping. If a robotic hand is designed based on the idea of caging, it would probably work well with noisy perception devices and low-quality control. This paper takes into account these merits and designs and implements a gripping hand based on the idea of caging. The gripping hand is concise and offers a low-cost alternative to co-operate with noisy data and low-quality control. According to previous work, we need four fingers to cage any 2D objects. That is to say, if each finger has one, two or three degree of freedoms, we will totally need four, eight or twelve actuators. The large number of actuators would be costly. This paper simplify the number of actuators into one by quantitatively analyzing finger formations with caging tests conducted on both random objects and objects from MPEG-7 shape database. It successfully lowers costs while maintains high performance. Following the simplified one-actuator design we implement a gripping hand by modifying a SCHUNK RH707 hand and carried out experiments with a manipulator built on the Neuronics Katana arm. The one-actuator gripping hand could work well with common depth cameras (Swiss Ranger) and pick up various objects. It bridges the gap between caging theories and applications and demonstrates the merits of caging.
机译:CAIPE可以为掌握中的不确定性提供鲁棒性。如果基于CACAGE的想法设计了机器人手,它可能与嘈杂的感知设备和低质量控制很好。本文考虑了这些优点和设计,并根据CACAGE的想法实现了抓握手。抓握手简明扼要,提供了低成本的替代方案,可以与嘈杂的数据和低质量控制合作。根据以前的工作,我们需要四个手指来笼中任何2D对象。也就是说,如果每个手指有一个,两到三度自由,我们将完全需要四个,八个或十二个执行器。大量的执行器将是昂贵的。本文通过定量分析在MPEG-7形状数据库中的随机对象和对象上进行的持续测试的手指形成,将致动器数量简化为一个。它成功降低了成本,同时保持高性能。在简化的一次执行器设计之后,我们通过修改Schunk RH707手来实现抓握手,并用内置在神经元皮纳纳臂上的操纵器进行实验。一致夹持器可以用普通的深度相机(瑞士游侠)良好,拿起各种物体。它弥补了Caping理论和应用程序之间的差距,并展示了Caiving的优点。

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