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Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 4491))

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Abstract

In a sinusoid like curve configuration, the snake-like manipulator (also called snake arm) has a wide range of potential applications for its redundancy to overcome conventional industrial robot’s limitation when carrying out a complex task. It can perform many kinds of locomotion like the nature snake or the animal’s tentacle to avoid obstacles, follow designated trajectories, and grasp objects. Effectively control of the snake-like manipulator is difficult for its redundancy. In this study, we propose an approach based on BP neural network to kinematic control the hyper-redundant snake-like manipulator. This approach, inspired by the Serpenoid curve and the concertina motion principle of the nature snake, is completely capable of solving the control problem of a planar snake-like manipulator with any number of links following any desired direction and trajectory. With shape transformation and base rotation, the manipulator’s configuration changes accordingly and moves actively to perform the designated tasks. By using BP neural networks in modeling the inverse kinematics, this approach has such superiorities as few control parameters and high precision. Simulations have demonstrated that this control technique for the snake-like manipulator is available and effective.

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Author information

Authors and Affiliations

  1. Robotics Laboratory of Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China

    Jinguo Liu, Yuechao Wang, Bin Li & Shugen Ma

  2. Center for Promotion of the COE Program, Ritsumeikan University, Shiga-ken, Japan

    Shugen Ma

  3. Graduate School of Chinese Academy of Sciences, Bejing, China

    Jinguo Liu

Authors
  1. Jinguo Liu

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  2. Yuechao Wang

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  3. Bin Li

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  4. Shugen Ma

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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering (M/C 154), University of Illinois at Chicago, 851 S. Morgan Street, 60607-7053, Chicago, IL, USA

    Derong Liu

  2. School of Automation, Southeast University, 210096, Nanjing, China

    Shumin Fei

  3. Laboratory of Complex Systems, Institute of Automation, Chinese Adacemy of Sciences, 100080, Beijing, P. R. China

    Zeng-Guang Hou

  4. School of Information Science and Engineering, Northeast University, Shenyang, 110004, China

    Huaguang Zhang

  5. School of Electrical Engineering, Hohai University, Nanjing, 210098, China

    Changyin Sun

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© 2007 Springer-Verlag Berlin Heidelberg

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Liu, J., Wang, Y., Li, B., Ma, S. (2007). Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_90

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