Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning

TL;DR

This paper introduces a low-cost, under-actuated robotic gripper with force sensing capabilities achieved through deep learning, specifically LSTM, eliminating the need for force sensors and enabling versatile, stable grasping of various objects.

Contribution

The paper presents a novel under-actuated gripper design with integrated force feedback control using LSTM deep learning, without requiring force sensors, enhancing versatility and robustness.

Findings

Successfully grasped objects of large dimension ranges

Achieved accurate fingertip position and contact force estimation

Demonstrated high stability and robustness in grasping tasks

Abstract

We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers. Second, we devise theoretical models of kinematics and power transmission based on the proposed gripper, accurately obtaining fingertip positions and contact forces. Through coupling and decoupling of five-linkage mechanisms, the proposed gripper offers the expected capabilities of grasping payload/force/stability and objects with large dimension ranges. Third, to realize the force control, an LSTM model is proposed to determine the grasping mode for synthesizing force-feedback control policies that exploit contact sensing after outlining the uncertainty of currents using a statistical method. Finally, a series of experiments are implemented to measure quantitative indicators, such as the payload, grasping force, force sensing, grasping stability and the dimension ranges of objects to be grasped. Additionally, the grasping performance of the proposed gripper is verified experimentally to guarantee the high versatility and robustness of the proposed gripper.