TacPalm: A Soft Gripper With a Biomimetic Optical Tactile Palm for Stable Precise Grasping

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xuyang Zhang;Tianqi Yang;Dandan Zhang;Nathan F. Lepora
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引用次数: 0

Abstract

Manipulating fragile objects in environments such as homes and factories requires stable and gentle grasping along with precise and safe placement. Compared to traditional rigid grippers, the use of soft grippers reduces the control complexity and the risk of damaging objects. However, it is challenging to integrate camera-based optical tactile sensing into a soft gripper without compromising the flexibility and adaptability of the fingers, while also ensuring that the precision of tactile perception remains unaffected by passive deformations of the soft structure during object contact. In this article, we demonstrate a modular soft two-fingered gripper with a 3-D-printed optical tactile sensor (the TacTip) integrated into the palm. We propose a soft-grasping strategy that includes three functions: light contact detection, grasp pose adjustment, and loss-of-contact detection so that objects of different shapes and sizes can be grasped stably and placed precisely, which we test with both artificial and household objects. By sequentially implementing these three functions, the grasp success rate progressively improves from 45% without any functions, to 59% with light contact detection, 90% with grasp pose adjustment, and 97% with loss-of-contact detection, achieving a submillimeter placement precision. Overall, this work demonstrates the feasibility and utility of integrating optical tactile sensors into the palm of a soft gripper and of applicability to various types of soft manipulators. The proposed grasping strategy has potential applications in areas such as fragile product processing and home assistance.
TacPalm:具有生物仿真光学触觉手掌的软抓手,可实现稳定的精确抓取
在家庭和工厂等环境中操作易碎物品,需要稳定、轻柔的抓取以及精确、安全的放置。与传统的刚性机械手相比,软机械手的使用降低了控制的复杂性和损坏物体的风险。然而,如何在不影响手指灵活性和适应性的前提下,将基于摄像头的光学触觉传感技术集成到软机械手中,同时确保触觉感知的精度不受软结构在接触物体过程中的被动变形的影响,是一项具有挑战性的工作。在本文中,我们展示了一种模块化双指软抓手,手掌中集成了一个 3-D 打印光学触觉传感器(TacTip)。我们提出了一种软抓取策略,其中包括三种功能:光接触检测、抓取姿势调整和失接触检测,这样就能稳定地抓取不同形状和大小的物体,并精确地将其放置。通过依次实现这三种功能,抓取成功率从没有任何功能时的 45% 逐步提高到了有了光接触检测功能时的 59%、有了抓取姿势调整功能时的 90%、有了失接触检测功能时的 97%,实现了亚毫米级的放置精度。总之,这项工作证明了将光学触觉传感器集成到软抓手手掌中的可行性和实用性,以及对各种类型软机械手的适用性。所提出的抓取策略有望应用于易碎产品加工和家庭辅助等领域。
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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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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