An Underactuated Gripper based on Car Differentials for Self-Adaptive Grasping with Passive Disturbance Rejection

Qiujie Lu, Jinhong Wang, Zhuang Zhang, Genliang Chen, Hao Wang, Nicolás Rojas
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引用次数: 4

Abstract

We introduce an underactuated differential-based robot gripper able to perform self-adaptive grasping with passive disturbance rejection. The gripper utilises three car differential systems to achieve self-adaptiveness with a single actuator: a base differential for distributing power from the motor to the fingers, and two independent finger differentials for controlling the proximal and distal joints. Linear and torsional springs are cleverly added to these differentials to allow the return of the fingers and the gripper-object system to equilibrium, thus enabling the gripper rejecting unexpected external forces applied to the fingers after securing a grasp. This novel design provides passive disturbance rejection without implementing complicated control systems and is the main contribution of this paper. Moreover, the differentials allow the gripper to perform not only self-adaptive power grasp but also precision grasp, provide it with a large force transmission efficiency, and facilitate the prediction of grasping position. We analyse the static model of the introduced differential system and evaluate the gripper design via four sets of experiments. Numerical and empirical results clearly demonstrate the viability of the proposed grasper.
基于汽车差速器的欠驱动夹持器被动抗扰自适应抓取
我们介绍了一种欠驱动的基于微分的机器人夹持器,能够进行自适应抓取和被动干扰抑制。抓手利用三个汽车差速器系统,通过一个执行器实现自适应:一个基础差速器用于将动力从电机分配到手指,两个独立的手指差速器用于控制近端和远端关节。线性和扭转弹簧被巧妙地添加到这些差速器中,以允许手指和抓手-物体系统恢复平衡,从而使抓手在固定抓手后能够拒绝施加在手指上的意外外力。这种新颖的设计提供了被动的干扰抑制,而不需要复杂的控制系统,这是本文的主要贡献。此外,该差速器使夹持器既能进行自适应动力抓取,又能进行精确抓取,具有较大的力传递效率,便于抓取位置的预测。我们分析了所引入的微分系统的静态模型,并通过四组实验对夹持器的设计进行了评价。数值和实证结果清楚地证明了所提出的抓取器的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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