Skip to main content

Advertisement

SpringerLink
Log in

Genetic fuzzy-based method for training two independent robots to perform a cooperative task

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

When two skilled human workers cooperate on a task, such as moving a sofa through a tight doorway, they often infer what needs to be done without explicit communication because they have learned cooperation skills from their prior work or training. This paper extends that concept to a two-robot team. The robots are given the task to carry a large payload through a narrow doorway while avoiding obstacles within the room. System dynamics and sensor noise were included in the study. Each robot is independently controlled with the knowledge of the goal location, its own position, and the pose of the payload. The decentralized control uses a Genetic Fuzzy System for each robot to learn its own decision-making skill through a training process without a pre-planned motion trajectory. The introduction of a genetic algorithm adds efficiency to the process of determining the shape of the fuzzy logic membership functions by using an evolutionary search algorithm to tune each parameter in the fuzzy system simultaneously. The contribution of this paper is to illustrate how genetic training can tune a simple, decentralized Fuzzy Logic System based on a given scenario and then be used, unaltered, for a scenario beyond that for which it was trained. The extended scenarios introduce unknown obstacles, new sizes and mass properties for the robots and payload, and random initial positions. The effectiveness of this approach for a 2D case is determined by dynamic simulation with results starting at a 95% success rate for the baseline scenario and 84% for the scenario that was extended furthest from how it was originally trained.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

References

  1. Bechlioulis CP, Kyriakopoulos KJ (2018) Collaborative multi-robot transportation in obstacle-cluttered environments via implicit communication. Front Robot AI 5:90

    Article  Google Scholar 

  2. Castillo O, Martinez-Marroquin R, Melin P, Valdez F, Soria J (2012) Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Inf Sci 192:19–38

    Article  Google Scholar 

  3. Culbertson P, Schwager M (2018) Decentralized adaptive control for collaborative manipulation. In: 2018 IEEE international conference on robotics and automation (ICRA), pp. 278–285. IEEE

  4. Faisal M, Algabri M, Abdelkader BM, Dhahri H, Al Rahhal MM (2017) Human expertise in mobile robot navigation. IEEE Access 6:1694–1705

    Article  Google Scholar 

  5. Farivarnejad H, Wilson S, Berman S (2016) Decentralized sliding mode control for autonomous collective transport by multi-robot systems. In: 2016 IEEE 55th conference on decision and control (CDC), pp. 1826–1833. IEEE

  6. Franchi A, Petitti A, Rizzo A (2018) Distributed estimation of state and parameters in multiagent cooperative load manipulation. IEEE Trans Control Network Syst 6(2):690–701

    Article  MathSciNet  Google Scholar 

  7. Hossain MA, Ferdous I (2015) Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. Robot Auton Syst 64:137–141

    Article  Google Scholar 

  8. Huang CA, Juang CF (2017) Evolutionary fuzzy control of two cooperative object-carrying wheeled robots for wall following through multiobjective continuous aco. In: 2017 Joint 17th world congress of international fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS), pp. 1–3. IEEE

  9. Juang CF, Lai MG, Zeng WT (2014) Evolutionary fuzzy control and navigation for two wheeled robots cooperatively carrying an object in unknown environments. IEEE Trans Cybern 45(9):1731–1743

    Article  Google Scholar 

  10. Lafta HA, Hassan ZF (2015) Mobile robot control using fuzzy logic. J Univ Babylon 23:524–532

    Google Scholar 

  11. Lee G, Chwa D (2018) Decentralized behavior-based formation control of multiple robots considering obstacle avoidance. Intell Serv Robot 11(1):127–138

    Article  Google Scholar 

  12. Lin CH, Wang SH, Lin CJ (2018) Interval type-2 neural fuzzy controller-based navigation of cooperative load-carrying mobile robots in unknown environments. Sensors 18(12):4181

    Article  Google Scholar 

  13. Luviano-Cruz D, Garcia-Luna F, Pérez-Domínguez L, Gadi SK (2018) Multi-agent reinforcement learning using linear fuzzy model applied to cooperative mobile robots. Symmetry 10(10):461

    Article  Google Scholar 

  14. Machado T, Malheiro T, Monteiro S, Erlhagen W, Bicho E (2016) Multi-constrained joint transportation tasks by teams of autonomous mobile robots using a dynamical systems approach. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 3111–3117. IEEE

  15. Marino A (2017) Distributed adaptive control of networked cooperative mobile manipulators. IEEE Trans Control Syst Technol 26(5):1646–1660

    Article  Google Scholar 

  16. Martínez-Soto R, Castillo O, Castro JR (2014) Genetic algorithm optimization for type-2 non-singleton fuzzy logic controllers. Recent Adv Hybrid Approaches Des Intell Syst. https://doi.org/10.1007/978-3-319-05170-3_1

    Article  Google Scholar 

  17. Omrane H, Masmoudi MS, Masmoudi M (2016) Fuzzy logic based control for autonomous mobile robot navigation. Comput Intell Neurosci. https://doi.org/10.1155/2016/9548482

    Article  Google Scholar 

  18. Pandey A, Parhi DR (2017) Optimum path planning mobile robot in unknown static and dynamic environments using fuzzy-wind driven optimization algorithm. Def Technol 13:47–58

    Article  Google Scholar 

  19. Petitti A, Franchi A, Di Paola D, Rizzo A (2016) Decentralized motion control for cooperative manipulation with a team of networked mobile manipulators. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 441–446. IEEE

  20. Ren W, Cao Y (2010) Distributed coordination of multi-agent networks: emergent problems, models, and issues. Springer, Berlin

    MATH  Google Scholar 

  21. Sathyan A, Ma O (2019) Collaborative control of multiple robots using genetic fuzzy systems. Robotica 37(11):1922–1936

    Article  Google Scholar 

  22. Singh NH, Thongam K (2017) Fuzzy logic-genetic algorithm-neural network for mobile robot navigation: a survey. Int Res J Eng Technol 4(8):24–45

    Google Scholar 

  23. Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  24. Sun Y, Barth A, Ma O (2020) An intelligent approach for a two-robot team to perform a cooperative task. In: AIAA Scitech 2020 Forum. p. 1116

  25. The Mathworks Inc. (R2019b) Matlab genetic algorithm and global optimaztion toolbox. https://www.mathworks.com/help/gads/ga.html

  26. Tsai CC, Wu HL, Tai FC, Chen,YS (2016) Decentralized cooperative transportation with obstacle avoidance using fuzzy wavelet neural networks for uncertain networked omnidirectional multi-robots. In: 2016 12th IEEE international conference on control and automation (ICCA), pp. 978–983. IEEE

  27. Tsiamis A, Bechlioulis CP, Karras GC, Kyriakopoulos KJ (2015) Decentralized object transportation by two nonholonomic mobile robots exploiting onlyh implicit communication. In: 2015 IEEE international conference on robotics and automation. pp. 171–176

  28. Wang Z, Schwager M (2016) Kinematic multi-robot manipulation with no communication using force feedback. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 427–432. IEEE

  29. Zadeh LA, Klir GJ, Yuan B (1996) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers. World Scientific, Singapore

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Cincinnati, Cincinnati, USA

    Andrew Barth, Yufeng Sun, Lin Zhang & Ou Ma

Authors
  1. Andrew Barth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ou Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Barth.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barth, A., Sun, Y., Zhang, L. et al. Genetic fuzzy-based method for training two independent robots to perform a cooperative task. Intel Serv Robotics 14, 535–548 (2021). https://doi.org/10.1007/s11370-021-00379-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-021-00379-2

Keywords

Search

Navigation