Tactile-Driven Gentle Grasping for Human-Robot Collaborative Tasks

被引:1
|
作者
Ford, Christopher J. [1 ,2 ]
Li, Haoran [1 ,2 ]
Lloyd, John [1 ,2 ]
Catalano, Manuel G. [5 ]
Bianchi, Matteo [3 ,4 ]
Psomopoulou, Efi [1 ,2 ]
Lepora, Nathan F. [1 ,2 ]
机构
[1] Univ Bristol, Dept Engn Math, Bristol, Avon, England
[2] Univ Bristol, Bristol Robot Lab, Bristol, Avon, England
[3] Univ Pisa, Dept Informat Engn, Pisa, Italy
[4] Univ Pisa, Res Ctr E Piaggio, Pisa, Italy
[5] IIT, Dept Soft Robot Human Cooperat & Rehabil, Genoa, Italy
基金
英国工程与自然科学研究理事会;
关键词
SLIP DETECTION; SENSORS; DESIGN; HANDS;
D O I
10.1109/ICRA48891.2023.10161036
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a control scheme for force sensitive, gentle grasping with a Pisa/IIT anthropomorphic SoftHand equipped with a miniaturised version of the TacTip optical tactile sensor on all five fingertips. The tactile sensors provide high-resolution information about a grasp and how the fingers interact with held objects. We first describe a series of hardware developments for performing asynchronous sensor data acquisition and processing, resulting in a fast control loop sufficient for real-time grasp control. We then develop a novel grasp controller that uses tactile feedback from all five fingertip sensors simultaneously to gently and stably grasp 43 objects of varying geometry and stiffness, which is then applied to a human-to-robot handover task. These developments open the door to more advanced manipulation with underactuated hands via fast reflexive control using high-resolution tactile sensing.
引用
收藏
页码:10394 / 10400
页数:7
相关论文
共 50 条
  • [1] Benchmarking human-robot collaborative assembly tasks
    Duarte, Laura
    Neves, Miguel
    Neto, Pedro
    [J]. RESULTS IN ENGINEERING, 2024, 22
  • [2] Human Intention Prediction in Human-Robot Collaborative Tasks
    Wang, Weitian
    Li, Rui
    Chen, Yi
    Jia, Yunyi
    [J]. COMPANION OF THE 2018 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'18), 2018, : 279 - 280
  • [3] Human Intention-Driven Learning Control for Trajectory Synchronization in Human-Robot Collaborative Tasks
    Ravichandar, Harish Chaandar
    Trombetta, Daniel
    Dani, Ashwin P.
    [J]. IFAC PAPERSONLINE, 2019, 51 (34): : 1 - 7
  • [4] Safety Protocol for Collaborative Human-Robot Recycling Tasks
    Medina, Angie C.
    Mora, Juan F.
    Martinez, Carol
    Barrero, Nicolas
    Hernandez, Wilson
    [J]. IFAC PAPERSONLINE, 2019, 52 (13): : 2008 - 2013
  • [5] Path Following Control for Human-Robot Collaborative Tasks
    Dubay, Shaundell
    Melek, William
    Nielsen, Christopher
    [J]. 2022 17TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), 2022, : 931 - 936
  • [6] Design of a Collaborative Architecture for Human-Robot Assembly Tasks
    El Makrini, Ilias
    Merckaert, Kelly
    Lefeber, Dirk
    Vanderborght, Bram
    [J]. 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, : 1624 - 1629
  • [7] Augmented Reality as a Medium for Human-Robot Collaborative Tasks
    Chacko, Sonia Mary
    Kapila, Vikram
    [J]. 2019 28TH IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), 2019,
  • [8] Learning Human Contribution Preferences in Collaborative Human-Robot Tasks
    Zhao, Michelle
    Simmons, Reid
    Admoni, Henny
    [J]. CONFERENCE ON ROBOT LEARNING, VOL 229, 2023, 229
  • [9] Human Motion Trajectory Prediction in Human-Robot Collaborative Tasks
    Li, Shiqi
    Wang, Haipeng
    Zhang, Shuai
    Wang, Shuze
    Han, Ke
    [J]. 2019 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS AND TECHNOLOGIES (AIAAT 2019), 2019, 646
  • [10] Brainwaves driven human-robot collaborative assembly
    Mohammed, Abdullah
    Wang, Lihui
    [J]. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2018, 67 (01) : 13 - 16