Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot

  • Anis Koubaa
  • Hachemi Bennaceur
  • Imen Chaari
  • Sahar Trigui
  • Adel Ammar
  • Mohamed-Foued Sriti
  • Maram Alajlan
  • Omar Cheikhrouhou
  • Yasir Javed
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 772)

Abstract

The multi-robot task allocation is a fundamental problem in robotics research area. Indeed, robots are typically intended to collaborate together to achieve a given goal. This chapter studies the performance of the IDBM, CM-MTSP, FL-MTSP, and Move-and-Improve approaches. In order to highlight the performance of the proposed schemes, we compared each one to appropriate existing ones. IDMB was compared with the RTMA [1], CM-MTSP was compared with single-objective and greedy algorithms, and FL-MTSP was compared with a centralized approach based on genetic algorithm and with NSGA-II algorithm. To validate the efficiency of the Move-and-Improve distributed algorithm, we first conducted extensive simulations and evaluated its performance in terms of the total traveled distance and the ratio of overlaped targets under different settings. The simulation results show that IDMB and Move-and-Improve algorithms produce near-optimal solutions. Also, CM-MTSP and FL-MTSP provide a good trade-off between conflicting objectives.

References

  1. 1.
    Viguria, Antidio, and Ayanna M. Howard. 2009. An integrated approach for achieving multirobot task formations. IEEE/ASME Transactions on Mechatronics 14 (2): 176–186.CrossRefGoogle Scholar
  2. 2.
    Ann Shim, Vui, KC Tan, and CY Cheong. 2012. A hybrid estimation of distribution algorithm with decomposition for solving the multiobjective multiple traveling salesman problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5): 682–691.Google Scholar
  3. 3.
    Ke, Liangjun, Qingfu Zhang, and Roberto Battiti. 2013. Moea/d-aco: A multiobjective evolutionary algorithm using decomposition and antcolony. IEEE Transactions on Cybernetics 43 (6): 1845–1859.CrossRefGoogle Scholar
  4. 4.
    Cheikhrouhou, Omar, Anis Koubâa, and Hachemi Bennaceur. 2014. Move and improve: A distributed multi-robot coordination approach for multiple depots multiple travelling salesmen problem. In 2014 IEEE international conference on autonomous robot systems and competitions (ICARSC), 28–35. IEEE.Google Scholar
  5. 5.
    Kivelevitch, Elad, Kelly Cohen, and Manish Kumar. 2013. A market-based solution to the multiple traveling salesmen problem. Journal of Intelligent and Robotic Systems: 1–20.Google Scholar
  6. 6.
    Miettinen, Kaisa. 2012. Nonlinear multiobjective optimization, vol. 12. Berlin: Springer Science & Business Media.MATHGoogle Scholar
  7. 7.
    Kuhn, W., and Harold. 1955. The hungarian method for the assignment problem. Naval Research Logistics (NRL) 2 (1–2): 83–97.Google Scholar
  8. 8.
    Ackerman, Evan. 2012. TurtleBot. http://www.turtlebot.com/. Accessed on Oct 2012.
  9. 9.
    Robot Operating System (ROS). http://www.ros.org.
  10. 10.
    Koubaa, Anis. 2014. The Iroboapp Pproject. http://www.iroboapp.org. Accessed 27 Jan 2016.
  11. 11.
  12. 12.
    Alexis, Kostas, Georgios Darivianakis, Michael Burri, and Roland Siegwart. 2016. Aerial robotic contact-based inspection: Planning and control. Autonomous Robots 40 (4): 631–655.CrossRefGoogle Scholar
  13. 13.
    Yong, Wang. 2015. Hybrid max-min ant system with four vertices and three lines inequality for traveling salesman problem. Soft Computing 19 (3): 585–596.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kivelevitch, Elad. 2011. Mdmtspv_ga - multiple depot multiple traveling salesmen problem solved by genetic algorithm. http://www.mathworks.com/matlabcentral/fileexchange/31814-mdmtspv-ga-multiple-depot-multiple-traveling-salesmen-problem-solved-by-genetic-algorithm.
  15. 15.
    Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T.A.M.T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6 (2): 182–197.CrossRefGoogle Scholar
  16. 16.
    Bolaños, R., M. Echeverry, and J. Escobar. 2015. A multiobjective non-dominated sorting genetic algorithm (nsga-ii) for the multiple traveling salesman problem. Decision Science Letters 4 (4): 559–568.CrossRefGoogle Scholar
  17. 17.
    Webots: the mobile robotics simulation software. 2014. http://www.cyberbotics.com/.
  18. 18.
  19. 19.
    Webots simulation scenarios. 2014. http://www.iroboapp.org/index.php?title=Videos.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anis Koubaa
    • 1
  • Hachemi Bennaceur
    • 2
  • Imen Chaari
    • 3
  • Sahar Trigui
    • 3
  • Adel Ammar
    • 2
  • Mohamed-Foued Sriti
    • 2
  • Maram Alajlan
    • 2
  • Omar Cheikhrouhou
    • 4
  • Yasir Javed
    • 5
  1. 1.Prince Sultan UniversityRiyadhSaudi Arabia
  2. 2.College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia
  3. 3.University Campus of ManoubaManoubaTunisia
  4. 4.College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia
  5. 5.College of Computer and Information SciencesPrince Sultan UniversityRiyadhSaudi Arabia

Personalised recommendations