Development of a General Search Based Path Follower in Real Time Environment

  • B. B. V. L. Deepak
  • G. Raviteja
  • Upasana Behera
  • Ravi Prakash
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)


The path planning problem of an Unmanned Ground Vehicle in a predefined structured environment is dealt in this paper. Here the environment chosen as the roadmap of NIT Rourkela obtained from Google maps as reference. An Unmanned Ground Vehicle (UGV) is developed and programmed so as to move autonomously from an indicated source location to the defined destination in the given map following the most optimal path. An algorithm based on linear search is implemented to the autonomous robot to generate shortest paths in the environment. The developed algorithm is verified with the simulations as well as in experimental environments.


Unmanned ground vehicle NITR map Path planning MATLAB simulation 


  1. 1.
    Fan, K. C., & Lui, P. C. (1994). Solving find path problem in mapped environments using modified A* algorithm. Systems, Man and Cybernetics, IEEE Transactions on, 24(9), 1390–1396.Google Scholar
  2. 2.
    Deepak, B. B. & Parhi, D. (2013). Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intelligent Service Robotics, 6(3), 155–162.Google Scholar
  3. 3.
    Deepak, B. B. V. L., Parhi, D. R., & Raju, B. M. V. A. (2014). Advance particle swarm optimization-based navigational controller for mobile robot. Arabian Journal for Science and Engineering, 39(8), 6477–6487.Google Scholar
  4. 4.
    Brooks, R. A. (1982). Solving the find-path problem by representing i’ree space as generalized cones, Artificial Intelligence Laboratory, Massachusetts Institute of I Technology. Al Memo 674, May.Google Scholar
  5. 5.
    Deepak, B. B. V. L., Parhi, D. R., & Kundu, S. (2012). Innate immune based path planner of an autonomous mobile robot. Procedia Engineering, 38, 2663–2671.Google Scholar
  6. 6.
    Deepak, B. B. V. L., & Parhi, D. R. (2013, December). Target seeking behaviour of an intelligent mobile robot using advanced particle swarm optimization. In Control, Automation, Robotics and Embedded Systems, International Conference on (pp. 1–6).Google Scholar
  7. 7.
    B. B. V. L., & Parhi, D. (2012). PSO based path planner of an autonomous mobile robot. Open Computer Science, 2(2), 152–168.Google Scholar
  8. 8.
    Kundu, S., Parhi, R., & Deepak, B. B. (2012). Fuzzy-neuro based navigational strategy for mobile robot. International Journal of Scientific & Engineering Research, 3(6).Google Scholar
  9. 9.
    Pakdaman, M., & Sanaatiyan, M. M. (2009, December). Design and implementation of line follower robot. In Computer and Electrical Engineering, 2009. ICCEE’09. Second International Conference on (Vol. 2, pp. 585–590). IEEE.Google Scholar
  10. 10.
    Bajestani, S. E. M., & Vosoughinia, A. (2010, August). Technical report of building a line follower robot. In Electronics and Information Engineering (ICEIE), 2010 International Conference On (Vol. 1, pp. V1–1). IEEE.Google Scholar
  11. 11.
    Deepak, B. B. V. L., Parhi, D. R., & Jha, A. K. (2011). Kinematic Model of Wheeled Mobile Robots. Int. J. on Recent Trends in Engineering & Technology, 5(04).Google Scholar
  12. 12.
    Parhi, D. R., & Deepak, B. B. V. L. (2011). Kinematic model of three wheeled mobile robot. Journal of Mechanical Engineering Research, 3(9), 307–318.Google Scholar
  13. 13.
    Deepak, B. B. V. L., Parhi, D. R., & Amrit, A. (2012). Inverse Kinematic Models for Mobile Manipulators. Caspian Journal of Applied Sciences Research, 1(13).Google Scholar

Copyright information

© Springer India 2017

Authors and Affiliations

  • B. B. V. L. Deepak
    • 1
  • G. Raviteja
    • 1
  • Upasana Behera
    • 1
  • Ravi Prakash
    • 1
  1. 1.Department of Industrial DesignNIT RourkelaRourkelaIndia

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