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Flexible robotic grasping strategy with constrained region in environment
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  • Research Article
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  • Published: 04 July 2017

Flexible robotic grasping strategy with constrained region in environment

  • Chao Ma  ORCID: orcid.org/0000-0002-6950-04981,2,
  • Hong Qiao2,
  • Rui Li2 &
  • …
  • Xiao-Qing Li1 

International Journal of Automation and Computing volume 14, pages 552–563 (2017)Cite this article

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Abstract

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.

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Author information

Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Chao Ma & Xiao-Qing Li

  2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Chao Ma, Hong Qiao & Rui Li

Authors
  1. Chao Ma
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  2. Hong Qiao
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  4. Xiao-Qing Li
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Corresponding author

Correspondence to Chao Ma.

Additional information

This work was supported by National Natural Science Foundation of China (No. 61210009), Beijing Municipal Science and Technology (Nos.D16110400140000 and D161100001416001), Fundamental Research Funds for the Central Universities (No. FRF-TP-15-115A1), and the Strategic Priority Research Program of the CAS (No.XDB02080003).

Recommended by Associate Editor Veljko Potkonjak

Chao Ma received the B. Sc. degree in automation from Central South University, China in 2007, the M. Sc. degree and the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, China in 2010 and 2015. He is a member of IEEE.

His research interests include intelligent robot systems, intelligent agents and robot control.

Hong Qiao received the B. Eng. degree in hydraulics and control and the M.Eng. degree in robotics from Xi’an Jiaotong University, China in 1986 and 1989, received the M.Phil. degree in robotics control from the Industrial Control Center, University of Strathclyde, UK in 1992, and received the Ph.D. degree in robotics and artificial intelligence from De Montfort University, UK in 1995. She is currently a “100-Talents Project” Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. She is currently a member of the Administrative Committee of the IEEE Robotics and Automation Society (RAS), the Long Range Planning Committee, the Early Career Award Nomination Committee, Most Active Technical Committee Award Nomination Committee, and Industrial Activities Board for RAS. She received the Second Prize of 2014 National Natural Science Awards, First Prize of 2012 Beijing Science and Technology Award (for Fundamental Research) and the Second Prize of 2015 Beijing Science and Technology Award (for Technology Inventions). She is on the Editorial Boards of five IEEE Transactions and the Editor-in-Chief of Assembly Automation (SCI indexed).

Her research interests include pattern recognition, machine learning, bio-inspired intelligent robot, brain-like intelligence, robotics and intelligent agents.

Rui Li received the B.Eng. degree in automation engineering from the University of Electronic Science and Technology of China, China in 2013. Currently he is a Ph.D. degree candidate in intelligent robot with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), China and University of Chinese Academy of Sciences (UCAS), China. He is a student member of IEEE.

His research interests include intelligent robot system, highprecision assembly, compliant manipulation for robotic systems.

Xiao-Qing Li received the B.Eng. degree from School of Automation and Electrical Engineering University of Science and Technology Beijing, China, in 2015. Currently, she is a Ph. D. degree candidate in Robotics with the Intelligent robotics Center at the University of Science and Technology Beijing, China. She is a student member of IEEE.

Her research interests include robotics intelligence, robot grasp and high-precision assembly.

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Cite this article

Ma, C., Qiao, H., Li, R. et al. Flexible robotic grasping strategy with constrained region in environment. Int. J. Autom. Comput. 14, 552–563 (2017). https://doi.org/10.1007/s11633-017-1096-5

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  • Received: 30 November 2016

  • Accepted: 31 May 2017

  • Published: 04 July 2017

  • Issue Date: October 2017

  • DOI: https://doi.org/10.1007/s11633-017-1096-5

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Keywords

  • Grasping strategy
  • compliant grasping
  • dexterous robotic hands
  • attractive region in environment
  • constrained region in environment
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