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Recurrent Neural Network for Robot Path Planning

  • Conference paper
Parallel and Distributed Computing: Applications and Technologies (PDCAT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3320))

Abstract

A novel model of organized neural network is shown to be very effective for path planning and obstacle avoidance in an unknown map which is represented by topologically ordered neurons. With the limited information of neighbor position and distance of the target position, robot will autonomously provide a proper path with free-collision and no redundant exploring in the process of exploring. The computer simulation will illustrate the performance.

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References

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Fudan University, 200433, Shanghai, PRC

    Ni Bin, Chen Xiong & Zhang Liming

  2. Institute for Infocomm Research, 21 Heng Mui Keng Terrance, 119613, Singapore

    Xiao Wendong

Authors
  1. Ni Bin
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  2. Chen Xiong
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  3. Zhang Liming
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  4. Xiao Wendong
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Editor information

Editors and Affiliations

  1. Nanyang Centre for Supercomputing and Visualisation School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore

    Kim-Meow Liew

  2. School of Computer Science, The University of Adelaide,  

    Hong Shen

  3. Asia Pacific Science and Technology Center, Sun Microsystems Inc., 50 Nanyang Avenue, N3-1c-10, 639798, Singapore

    Simon See

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Wentong Cai

  5. Southwest Jiaotong University,  

    Pingzhi Fan

  6. Tohoku University ,  

    Susumu Horiguchi

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© 2004 Springer-Verlag Berlin Heidelberg

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Bin, N., Xiong, C., Liming, Z., Wendong, X. (2004). Recurrent Neural Network for Robot Path Planning. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_43

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  • DOI: https://doi.org/10.1007/978-3-540-30501-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24013-6

  • Online ISBN: 978-3-540-30501-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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