Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning | Journal of Ambient Intelligence and Humanized Computing Skip to main content
Springer Nature Link
Log in

Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Using mobile robots in disaster areas can reduce risks and the search time in urban search and rescue operations. Optimal path-planning for mobile robotics can play a key role in the reduction of the search time for rescuing victims. In order to minimize the search time, the shortest path to the target should be determined. In this paper, a new integrated Reinforcement Learning—based method is proposed to search and find a hidden target in an unknown environment in the minimum time. The proposed algorithm is developed in two main phases. Depending on whether or not the mobile robot receives the signal from the hidden target, phases I or II of the proposed algorithm can be carried out. Then, the proposed algorithm is implemented on an e-puck robot in an urban environment which is simulated within Webots software. Finally, to demonstrate the efficiency of the proposed method and to verify it, the computational results from the proposed method are compared with three conventional methods from the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Casper J, Murphy RR (2003) Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center. IEEE Trans Syst Man Cybern Part B (Cybern) 33:367–385. https://doi.org/10.1109/TSMCB.2003.811794

  • Cormen TH, Stein C, Rivest RL, Leiserson CE (2001) Introduction to algorithms. McGraw-Hill Higher Education, New York

    MATH  Google Scholar 

  • Faigl J, Macák J, Multi-Goal (2011) Path planning using self-organizing map with navigation functions. In: Proceedings of 19th European symposium on artificial neural networks, Bruges, Belgium, April 27–29

  • Fakoor M, Kosari A, Jafarzadeh M (2015) Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment. Int J Adv Mechatron Syst 6:174–183. https://doi.org/10.1504/IJAMECHS.2015.072707

    Article  Google Scholar 

  • Felner A (2011) Position paper: Dijkstra’s algorithm versus uniform cost search or a case against Dijkstra’s algorithm. In: Fourth annual symposium on combinatorial search

  • Geng N, Gong DW, Zhang Y (2014) PSO-based robot path planning for multisurvivor rescue in limited survival. Time Math Probl Eng 2014:10. https://doi.org/10.1155/2014/187370

    Google Scholar 

  • Jafarzadeh H, Gholami S, Bashirzadeh R (2014) A new effective algorithm for on-line robot motion planning. Decis Sci Lett 3:121–130

    Article  Google Scholar 

  • Khanmohammadi S, Zarrin RS (2011) Intelligent path planning for rescue robot. In: Proceedings of world academy of science, engineering and technology. pp 764–769

  • Lee L (2015) Robotic search & rescue via online multi-task reinforcement learning. arXiv:1511.08967

  • Lodeiro-Santiago M, Santos-González I, Caballero-Gil P, Caballero-Gil C (2017) Secure system based on UAV and BLE for improving SAR missions. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0603-4

    Google Scholar 

  • Majumder S, Kar S (2017) Multi-criteria shortest path for rough graph. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0601-6

    Google Scholar 

  • Mehlhorn K, Sanders P (2008) Algorithms and data structures: the basic toolbox. Springer, Berlin, Heidelberg

    MATH  Google Scholar 

  • Mondada F et al. (2009) The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th conference on autonomous robot systems and competitions. vol LIS-CONF-2009-004. IPCB, Castelo Branco. pp 59–65

    Google Scholar 

  • Pascarella D, Venticinque S, Aversa R, Mattei M, Blasi L (2015) Parallel and distributed computing for UAVs trajectory planning. J Ambient Intell Humaniz Comput 6:773–782. https://doi.org/10.1007/s12652-015-0282-y

    Article  Google Scholar 

  • Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. Bradford Book. The MIT Press, Cambridge, MA

  • Williams RL, Wu J (2010) Dynamic obstacle avoidance for an omnidirectional mobile robot. J Robot. https://doi.org/10.1155/2010/901365

    Google Scholar 

  • Yamauchi B (1998) Frontier-based exploration using multiple robots. In: Proceedings of the second international conference on Autonomous agents. ACM, pp 47–53

  • Zeng W, Church RL (2009) Finding shortest paths on real road networks: the case for A*. Int J Geogr Inf Sci 23:531–543. https://doi.org/10.1080/13658810801949850

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran

    S. M. Sombolestan, A. Rasooli & S. Khodaygan

Authors
  1. S. M. Sombolestan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Rasooli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Khodaygan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Khodaygan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sombolestan, S.M., Rasooli, A. & Khodaygan, S. Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning. J Ambient Intell Human Comput 10, 1841–1850 (2019). https://doi.org/10.1007/s12652-018-0777-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0777-4

Keywords

Search

Navigation