Advertisement

Collaborative and Non-Collaborative Dynamic Path Prediction Algorithm for Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 152)

Abstract

In this research the extension of the algorithm for dynamic collaborative path prediction for mobile agents is proposed. This algorithm is inspired by human behavior in group of dynamical obstacles. Mobile agent in collaborative manner uses coordinates of other mobile agents in the same environment to calculate and based on statistical methods predict future path of other objects. For this purpose spatial–temporal variables are decomposed in order to optimize the method and to make it more efficient. This algorithm can be used in mobile robotics, automobile industry and aeronautics. Moreover this method allows full decentralization of collision detection which allows many advantages from minimizing of network traffic to simplifying of inclusion of additional agents in relevant space. Implementation of the algorithm will be low resource consuming allowing mobile agents to free resources for additional tasks.

Keywords

Collaborative dynamic path prediction Mobile agents Mobile robots Human motion Statistical methods in collision detection Relevant predicted collision time Regression analysis 3D 

References

  1. 1.
    Umedachi T, Takeda K, Nakagaki T, Kobayashi R, Ishiguro A (2010) Fully decentralized control of a soft-bodied robot inspired by true slime mold. Biol Cybern 102(3):261–269. Available from: academic search complete, Ipswich, MA. Accessed 12 May 2010Google Scholar
  2. 2.
    Feng L et al (1993) Cross-couping motion controller for mobile robots. IEEE control systems, 0272-1708/93Google Scholar
  3. 3.
    Forsberg J et al (1995) Mobile robot navigation using the range-weighted Hough transform. IEEE Robot Autom Mag 2:18–25Google Scholar
  4. 4.
    Gat E (1998) Three-layer architectures. Artifical intelligence and mobile robots, AAAI/The MIT Press, Cambridge, pp 195–210Google Scholar
  5. 5.
    Luger GF, Stubblefield WA (1998) Artifical intelligence. Addison Wesley Lingman, Reading, pp 247–292Google Scholar
  6. 6.
    Thrun S et al (2006) Probabilistic robotics. Massachusetts Institute of TechnologyGoogle Scholar
  7. 7.
    Graf B et al (2004) Mobile robot assistants. IEEE Robot Autom Mag 1070-9932/04:68–69Google Scholar
  8. 8.
    Roy N et al (2002) Collaborative robot exploration and rendezvous algorithms, performance bounds and observations. ARJournal, Kluwer Academic Publishers, DordrechtGoogle Scholar
  9. 9.
    Roy N et al (2003) Planning under uncertainty for reliable health care robotics. In: The 4th international conference on field and service robotics, Pittsburgh, 14–16 July 2003Google Scholar
  10. 10.
    Breazeal C et al (2007) Efficient model learning for dialog management. In: 2nd ACM/IEEE international conference on human-robot interaction, Arlington, 10–12 March 2007Google Scholar
  11. 11.
    Roy N, McCallum A (2001) Toward optimal active learning through monte carlo estimation of error reduction. In: Proceedings of the international conference on machine learning (ICML 2001), WilliamstownGoogle Scholar
  12. 12.
    Roy N, Gordon G (2002) Exponential family PCA for belief compression in POMDPs. In: Advances in neural information processing (15) NIPS, Vancouver, Dec 2002Google Scholar
  13. 13.
    Brunskill E (2008) CORL: a continuous-state offset-dynamics reinforcement learner, UAIGoogle Scholar
  14. 14.
    Klapka P (2001) Kybernetika a umělá inteligenceGoogle Scholar
  15. 15.
    van Waveren JMP, Rothkrantz LJM (2008) Automated static and dynamic obstacle avoidance in arbitrary 3D polygonal worlds. Mobile robots motion planning new challenges, pp 455–468Google Scholar
  16. 16.
    Gnadt W, Grossberg S (2008) SOVEREIGN: an autonomuous neural system for incrementally learning to navigate towards a rewarded goal. Mobile robots motion planning new challenges, pp 99–119Google Scholar
  17. 17.
    Casini M, Garulli A, Giannitrapani A, Vicino A (2009) A matlab-based remote lab for multi-robot experiments. In: 8th IFAC symposium on advances in control education, Kumamoto, 21–23 Oct 2009Google Scholar
  18. 18.
    Payá L, Reinoso O, Sánchez A, Gil A, Fernández L (2009) An educational tool for mobile robots remote interaction. In: 8th IFAC symposium on advances in control education, Kumamoto, 21–23 Oct 2009Google Scholar
  19. 19.
    Cezayirli A, Kerestecioglu F (2009) On preserving connectivity of autonomous mobile robots. In: IEEE international symposium on intelligent control, Saint Petersburg, 8–10 July 2009Google Scholar
  20. 20.
    Jonathan AR, Aeyels D (2009) Multi-robot coverage to locate fixed and moving targets. In: IEEE international symposium on intelligent control, Saint Petersburg, 8–10 July 2009Google Scholar
  21. 21.
    Parlaktuna O, Sipahioglu A, Kirlik G, Yazici A (2009) Multi-robot sensor-based coverage path planning using capacitated arc routing approach. In: IEEE international symposium on intelligent control, Saint Petersburg, 8–10 July 2009Google Scholar
  22. 22.
    Kurabayashi D et al (1994) Cooperative sweeping by multiple mobile robots. Proc IEEE Int Conf Robot Automat 3:1744–1749Google Scholar
  23. 23.
    Latimer D et al (2002) Towards sensor based coverage with robot teams. Proc IEEE Int Conf Robot Automat 1:961–967Google Scholar
  24. 24.
    Mei Y, Yung-Hsiang L, Charlie HY, George LCS (2006) Deployment of mobile robots with energy and timing constraints. IEEE Trans Robot Automat 22(3):507–522Google Scholar
  25. 25.
    M. Ozkan, A. Yazici, M. Kapanoglu, O. Parlaktuna (2009) A genetic algorithm for task completion time minimization for multi-robot sensor-based coverage. In: IEEE International symposium on intelligent control, Saint Petersburg, July 8–10Google Scholar
  26. 26.
    Ferrara A, Rubagotti M (2009) A dynamic obstacle avoidance strategy for a mobile robot based on sliding mode control. In: 18th IEEE international conference on control applications, Saint Petersburg, July 8–10Google Scholar
  27. 27.
    Teymur C, Temeltaş H (2010) A new behavior combining method for mobile robots. ITU journal series D: engineering, 1 Feb 2010Google Scholar
  28. 28.
    Jeong HK, Choi KH, Kim SH, Kwak YK (2008) Driving mode decision in the obstacle negotiation of a variable single-tracked robot. Adv Robot 22:1421–1438CrossRefGoogle Scholar
  29. 29.
    Babovic E (2011) Collaborative and non-collaborative dynamic path prediction algorithm for mobile agents collision detection with dynamic obstacles in a two-dimensional space, IEEM, SingaporeGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Faculty of Information TechnologiesMostarBosnia and Herzegovina

Personalised recommendations