Skip to main content
SpringerLink
Log in

Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot

  • Chapter
  • First Online:
Robot Path Planning and Cooperation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 772))

  • 2353 Accesses

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. Miettinen, Kaisa. 2012. Nonlinear multiobjective optimization, vol. 12. Berlin: Springer Science & Business Media.

    MATH  Google Scholar 

  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. Ackerman, Evan. 2012. TurtleBot. http://www.turtlebot.com/. Accessed on Oct 2012.

  9. Robot Operating System (ROS). http://www.ros.org.

  10. Koubaa, Anis. 2014. The Iroboapp Pproject. http://www.iroboapp.org. Accessed 27 Jan 2016.

  11. Helsgaun, Keld. 2012. Lkh. http://www.akira.ruc.dk/~keld/research/LKH/.

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  17. Webots: the mobile robotics simulation software. 2014. http://www.cyberbotics.com/.

  18. Pioneer3at robots. 2014. http://www.mobilerobots.com/ResearchRobots/P3AT.aspx.

  19. Webots simulation scenarios. 2014. http://www.iroboapp.org/index.php?title=Videos.

Download references

Author information

Authors and Affiliations

  1. Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

  2. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

    Hachemi Bennaceur, Adel Ammar, Mohamed-Foued Sriti & Maram Alajlan

  3. University Campus of Manouba, Manouba, Tunisia

    Imen Chaari & Sahar Trigui

  4. College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

    Omar Cheikhrouhou

  5. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Yasir Javed

Authors
  1. Anis Koubaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hachemi Bennaceur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Imen Chaari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sahar Trigui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adel Ammar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed-Foued Sriti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maram Alajlan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Omar Cheikhrouhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yasir Javed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anis Koubaa .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Koubaa, A. et al. (2018). Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot. In: Robot Path Planning and Cooperation. Studies in Computational Intelligence, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-77042-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77042-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77040-6

  • Online ISBN: 978-3-319-77042-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation