Automation, Control and Intelligent Systems

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Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment

Received: 28 December 2012    Accepted:     Published: 02 April 2013
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Abstract

This paper provides a new approach to the multi-robot path planning problem predicting the position of a dynamic obstacle which undergoes linear motion in the given workspace changing its direction at regular intervals of time. The prediction is done in order to avoid collision of the robots with the dynamic obstacle. First the work is done in simula-tion environment then the entire work has been implemented on Khepera II mobile robot. The performance of the above mentioned approach has been found to be satisfactory compared to the classical non-predictive approaches of dynamic obstacle avoidance.

DOI 10.11648/j.acis.20130102.11
Published in Automation, Control and Intelligent Systems (Volume 1, Issue 2, April 2013)
Page(s) 16-23
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Linear Prediction, Particle Swarm Optimization, Multi-Robot Motion Planning

References
[1] J. Kennedy, R. Eberhart, "Particle swarm optimization", In Proceedings of IEEE International conference on Neural Networks. (1995) 1942-1948.
[2] Jayasree Chakroborty, Amit Konar, Aruna Chakroborty, "Multi-robot co-operation by Swarm and Evolutionary Algorithms".
[3] M. Ryan, "Graph Decomposition for Efficient Multi-robot Path planning,"in Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2003-2008, Jan. 2007.
[4] A.Fujimori and S. Tani, ‘A navigation of mobile robots with collision avoidance for moving obstacles’, in Proc. IEEE International Conference on Industrial Technology, Bangkok, Thailand, Dec. 2002, pp. 16.
[5] F. van den Bergh and A.P. Engelbrecht, "Cooperative learning in neural networks using particle swarm optimizers," South African Computer Journal, 26:84-90, 2000.
[6] T. Tsubouchi and M. Rude, "Motion planning for mobile robots in a time-varying environment", J. of Robotics and Mechatronics, Vol. 8, No. 1, pp. 15-24, 1996.
[7] S. Ishikawa, "A method of indoor mobile robot navigation by using fuzzy control", in Proc. IEEE/RSJ Int. Workshop on Intelligent Robots and Systems, pp. 1013-1018, 1991.
[8] F. Kunwar, F. Wong, R. Ben Mrad, B. Benhabib, "Guidance-based online robot motion planning for the interception of mobile targets in dynamic environments", Journal of Intelligent and Robotic Systems, Vol. 47, Issue 4, pp. 341-360, 2006.
[9] J. Minura, H. Uozumi, and Y. Shirai, "Mobile robot motion planning considering the motion uncertainty of moving obstacles", in Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, Tokyo, pp. 692-698, 1999.
Author Information
  • ETCE Department, Jadavpur University, Kolkata-700032

  • ETCE Department, Jadavpur University, Kolkata-700032

  • ETCE Department, Jadavpur University, Kolkata-700032

  • ETCE Department, Jadavpur University, Kolkata-700032

  • Jaya College of Engineering, Chennai, Tamil Nadu

Cite This Article
  • APA Style

    Suparna Roy, Dhrubojyoti Banerjee, Chiranjib Guha Majumder, Amit Amit Konar, R. Janarthanan. (2013). Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment. Automation, Control and Intelligent Systems, 1(2), 16-23. https://doi.org/10.11648/j.acis.20130102.11

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    ACS Style

    Suparna Roy; Dhrubojyoti Banerjee; Chiranjib Guha Majumder; Amit Amit Konar; R. Janarthanan. Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment. Autom. Control Intell. Syst. 2013, 1(2), 16-23. doi: 10.11648/j.acis.20130102.11

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    AMA Style

    Suparna Roy, Dhrubojyoti Banerjee, Chiranjib Guha Majumder, Amit Amit Konar, R. Janarthanan. Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment. Autom Control Intell Syst. 2013;1(2):16-23. doi: 10.11648/j.acis.20130102.11

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  • @article{10.11648/j.acis.20130102.11,
      author = {Suparna Roy and Dhrubojyoti Banerjee and Chiranjib Guha Majumder and Amit Amit Konar and R. Janarthanan},
      title = {Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment},
      journal = {Automation, Control and Intelligent Systems},
      volume = {1},
      number = {2},
      pages = {16-23},
      doi = {10.11648/j.acis.20130102.11},
      url = {https://doi.org/10.11648/j.acis.20130102.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acis.20130102.11},
      abstract = {This paper provides a new approach to the multi-robot path planning problem predicting the position of a dynamic obstacle which undergoes linear motion in the given workspace changing its direction at regular intervals of time. The prediction is done in order to avoid collision of the robots with the dynamic obstacle. First the work is done in simula-tion environment then the entire work has been implemented on Khepera II mobile robot. The performance of the above mentioned approach has been found to be satisfactory compared to the classical non-predictive approaches of dynamic obstacle avoidance.},
     year = {2013}
    }
    

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    AB  - This paper provides a new approach to the multi-robot path planning problem predicting the position of a dynamic obstacle which undergoes linear motion in the given workspace changing its direction at regular intervals of time. The prediction is done in order to avoid collision of the robots with the dynamic obstacle. First the work is done in simula-tion environment then the entire work has been implemented on Khepera II mobile robot. The performance of the above mentioned approach has been found to be satisfactory compared to the classical non-predictive approaches of dynamic obstacle avoidance.
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