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Precision Grasp Planning for Multi-Finger Hand to Grasp Unknown Objects

Published online by Cambridge University Press:  21 February 2019

Wenyu Yan*
Affiliation:
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China. E-mails: [email protected], [email protected]
Zhen Deng
Affiliation:
TAMs, Department of Informatics, University Hamburg, Hamburg, Germany E-mails: [email protected], [email protected]
Jinbao Chen
Affiliation:
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China. E-mails: [email protected], [email protected]
Hong Nie
Affiliation:
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China. E-mails: [email protected], [email protected]
Jianwei Zhang
Affiliation:
TAMs, Department of Informatics, University Hamburg, Hamburg, Germany E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

Determining an appropriate grasp configuration for multi-finger grasping is difficult due to the complexity of robotic hands. The multi-finger grasp planning should consider not only geometry constraints of objects but also kinematics and dynamics of robotic hand. In this paper, a precision grasp-planning framework is presented for multi-finger hand to grasp unknown objects. First, the manipulation capabilities of the robotic hand are analyzed. The analysis results are further used as bases for the precision grasp planning. Second, the superquadric (SQ) fitting method is used for approximating unknown object models. Finally, a local–global optimization method is implemented to find appropriate grasp configurations for dexterous hand. The presented planning framework is validated in simulation experiments. Simulation results demonstrated that the presented grasp-planning framework enables the multi-finger hand to grasp unknown objects effectively.

Type
Articles
Copyright
© Cambridge University Press 2019 

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