CBDF Based Cooperative Multi Robot Target Searching and Tracking Using BA

  • Sanjeev Sharma
  • Chiranjib Sur
  • Anupam Shukla
  • Ritu Tiwari
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Path planning is a problem where the objective to reach up to target from source without collide with obstacle This problem would be complex when it is considered with multi robot and unknown environment. Reaching up to the target is considered as an optimization problem where the objective to minimize the distance, time and energy. This paper use the Bat algorithm (BA) for the movement of robot form one location to next location with optimizes the time, distance and energy. Here the direction of the movement is given by clustering based distribution factors (CBDF) that guide the robot to move in different direction. Different parameters are calculated during the moving of robots that help to analyze the process of target searching and tracking. Simulation is done with both simple and complex environment and results shows that the method works in both cases in searching and tracking.

Keywords

Multi robot path planning Target tracking and searching Bat algorithm 

References

  1. 1.
    Prassler, E., Ritter, A., Schaeffer, C., Fiorini, P.: A short history of cleaning robots. Auton. Robots 9(3), 211–226 (2000)CrossRefGoogle Scholar
  2. 2.
    Marjovi, A., Nunes, J.G., Marques, L., de Almeida, A.: Multi-robot exploration and fire searching. In: International Conference on Intelligent Robots and Systems, IEEE/RSJ, pp. 1929–1934 (2009)Google Scholar
  3. 3.
    Lee, C.H., Kim, S.H., Kang, S.C., Kim, M.S., Kwak, Y.K.: Double-track mobile robot for hazardous environment applications. Adv. Robot. 17(5), 447–459 (2003)CrossRefGoogle Scholar
  4. 4.
    Kumar Das, P.: D* lite algorithm based path planning of mobile robot in static Environment. Int. J. Comput. Commun. Technol. (IJCCT) 2, 32–36 (2011)Google Scholar
  5. 5.
    Marzouqi, M.A.: Efficient path planning for searching a 2-D grid-based environment map. In: IEEE Conference and Exhibition (GCC) Dubai, United Arab Emirates, pp. 19–22 (2011)Google Scholar
  6. 6.
    Shwail, S.H., Karim, A., Turner, S.: Article: probabilistic multi robot path planning in dynamic environments: a comparison between A* and DFS. Int. J. Comput. Appl. 82(7), 29–34 (2013)Google Scholar
  7. 7.
    Ryan, M.R.K.: Exploiting subgraph structure in multi-robot path planning. J. Artif. Intell. Res. (JAIR) 31, 497–542 (2008)MATHGoogle Scholar
  8. 8.
    Kala, R.: Multi-robot path planning using co-evolutionary genetic programming. Expert Syst. Appl. 39(3), 3817–3831 (2012)CrossRefGoogle Scholar
  9. 9.
    Kim, U.-H., Lee, G., Hong, I., Kim, Y.-J., Kim, D.: New potential functions for multi robot path planning: SWARM or SPREAD. In: The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 2, pp. 557–561 (2010)Google Scholar
  10. 10.
    Chakraborty, J., Konar, A., Jain, L.C., Chakraborty, U.K.: Cooperative multi-robot path planning using differential evolution. J. Intell. Fuzzy Syst. 20(1), 13–27 (2009)MATHGoogle Scholar
  11. 11.
    Li, Rongxin, C., Chenguang, Y., Demin, X.: Multi-robot path planning based on the developed RRT* algorithm. In: 32nd Chinese Control Conference (CCC), pp. 7049–7053 (2013)Google Scholar
  12. 12.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, pp. 65–74 (2010)Google Scholar
  13. 13.
    Gandomi, A.H., Yang, X.S., Alavi, A.H., Talatahari, S.: Bat algorithm for constrained optimization tasks. Neural Comput. Appl. 22(6), 1239–1255 (2013)CrossRefGoogle Scholar
  14. 14.
    Tsai, P.W., Pan, J.S., Liao, B.Y., Tsai, M.J., Istanda, V.: Bat algorithm inspired algorithm for solving numerical optimization problems. Appl. Mech. Mater. 148, 134–137 (2012)Google Scholar
  15. 15.
    Wang, G., Guo, L., Duan, H., Liu, L., Wang, H.: A bat algorithm with mutation for UCAV path planning. Sci. World J. doi: 10.1100/2012/418946.2012
  16. 16.
    Khan, K., Sahai, A.: A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int. J. Intell. Syst. Appl. (IJISA) 4(7), 23–29 (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Sanjeev Sharma
    • 1
  • Chiranjib Sur
    • 1
  • Anupam Shukla
    • 1
  • Ritu Tiwari
    • 1
  1. 1.ABV- Indian Institute of Information Technolgy and ManagementGwaliorIndia

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