A minimizing force model based on bounded force closure in multi-fingered grasping deformable object

被引:1
|
作者
Cui, Tong [1 ]
Song, Aiguo [1 ]
Juan, Wu [1 ]
机构
[1] Southeast Univ, Coll Instrument Sci & Engn, Nanjing 210096, Peoples R China
关键词
force closure; multi-fingered grasping minimizing gasping angle criterion; Lagrange mixed constrains;
D O I
10.1109/ROBIO.2007.4522229
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Based on the characteristics of dexterous hand, a physically-based multi-fingered minimizing force model is proposed for the stable grasp of soft fingers on the surface of deformable object, whose goal is to establish a simple but robust control method for grasping manipulation. The trajectory of fingertips movement is introduced to show exact grasping positions, which is the base of force closure grasp. In addition, it is necessary to investigate the conditions for force closure in order to derive the properties of force closure grasp. To overcome the uncertainty in the solution of the general model and produce realistic force feedback for virtual hand grasp, optimized models with minimizing grasping angle criterion is performed to evaluate stability of grasping deformable object. Lagrange multiplier with mixed constrains is discussed to realize the optimization schemes to solve the Multisolvability. Finally, some graphical examples are included to demonstrate the effectiveness of the proposed method. Experimental results show that using the force generation and feedback method, the user can sense realistic contact forces via the Cyber Grasp data glove during the process of virtual grasp.
引用
收藏
页码:595 / 600
页数:6
相关论文
共 50 条
  • [21] Grasping Force Optimization for Multi-fingered Robotic Hands Using Projection and Contraction Methods
    Mu, Xuewen
    Zhang, Yaling
    JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2019, 183 (02) : 592 - 608
  • [22] Grasping Force Optimization for Multi-fingered Robotic Hands Using Projection and Contraction Methods
    Xuewen Mu
    Yaling Zhang
    Journal of Optimization Theory and Applications, 2019, 183 : 592 - 608
  • [23] Two recurrent neural networks for grasping force optimization of multi-fingered robotic hands
    Fok, LM
    Wang, J
    PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 35 - 40
  • [24] Grasping force control of multi-fingered robot hand based on slip detection using tactile sensor
    Gunji, Daisuke
    Mizoguchi, Yoshitorno
    Teshigawara, Seiichi
    Ming, Aiguo
    Namiki, Akio
    Ishikawaand, Masatoshi
    Shimojo, Makoto
    2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9, 2008, : 2605 - 2610
  • [25] Grasping Force Control of Multi-fingered Robot Hand based on Slip Detection Using Tactile Sensor
    Gunji, Daisuke
    Mizoguchi, Yoshitomo
    Teshigawara, Seiichi
    Ming, Aiguo
    Namiki, Akio
    Ishikawa, Masatoshi
    Shimojo, Makoto
    2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, 2008, : 858 - 863
  • [26] Real-time grasping force control in coordination of multi-fingered robot hands
    Zuo, Bingran
    Qian, Wenhan
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 32 (05): : 34 - 38
  • [27] A Grasping Force Optimization Algorithm with Dynamic Torque Constraints Selection for Multi-Fingered Robotic Hands
    Lippiello, Vincenzo
    Siciliano, Bruno
    Villani, Luigi
    2011 AMERICAN CONTROL CONFERENCE, 2011, : 1118 - 1123
  • [28] Model-based strategy for grasping 3D deformable objects using a multi-fingered robotic hand
    Zaidi, Lazher
    Corrales, Juan Antonio
    Bouzgarrou, Belhassen Chedli
    Mezouar, Youcef
    Sabourin, Laurent
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2017, 95 : 196 - 206
  • [29] Description and Analysis of Multi-Fingered Hand Grasping with a New Finger-Object Contact Model
    Zhang, Yin
    Zhan, Qiang
    Li, Chunhong
    INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, 2019, 16 (05)
  • [30] Nonlinear Disturbance Observer for Object Grasping/Manipulation by Multi-Fingered Robot Hand
    Ueki, S.
    Mouri, T.
    Kawasaki, H.
    IFAC PAPERSONLINE, 2017, 50 (01): : 12704 - 12709