Skip to main content
Log in

Fuzzy rule-based environment-aware autonomous mobile robots for actuated touring

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

A Correction to this article was published on 27 April 2022

This article has been updated

Abstract

The involvement of computer-programmed autonomous mobile robots in real-time activities is emerging in the recent years. The actuation and interaction of the robots are controlled through optimized high-level programming to respond to environmental factors. Such robots require an optimized touring plan with a better response to understand the inherent conditions. In this article, fuzzy rule-based optimization for actuated touring (FOAT) is presented. FOAT is responsible for balancing the actuation and response of the mobile robot agents in touring and path exploration. The touring and self-decision analysis of the programmed robots is improved through FOAT by adapting the environmental conditions and then constructing rules for response. Different from the functions of line-based or other robot touring, the proposed actuated touring frames decisive rules based on the varying inputs. With the framed rules, the touring process of the robot is modified to achieve the best solution instantly adaptive to the environment. The performance of FOAT is verified through experiments and is then analyzed using the metrics: touring cost, obstacles hit ratio, time-lapse, and tour length.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Change history

References

  1. Teixeira MAS, Santos HB, Dalmedico N, Arruda LVRD, Neves F, Oliveira ASD (2018) Intelligent environment recognition and prediction for NDT inspection through autonomous climbing robot. J Intell Rob Syst 92(2):323–342

    Article  Google Scholar 

  2. Shuai W, Chen X-P (2019) KeJia: towards an autonomous service robot with tolerance of unexpected environmental changes. Front Inf Technol Electron Eng 20(3):307–317

    Article  Google Scholar 

  3. Gómez Eguíluz A, Rañó I, Coleman SA, Mcginnity TM (2019) Reliable robotic handovers through tactile sensing. Autonom Robots 43(7):1623–1637

    Article  Google Scholar 

  4. To AWK, Paul G, Liu D (2018) A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots. Robot Comput Integr Manuf 49:13–23

    Article  Google Scholar 

  5. Zheng Z, Zhao H, Swanson AR, Weitlauf AS, Warren ZE, Sarkar N (2018) Design, development, and evaluation of a noninvasive autonomous robot-mediated joint attention intervention system for young children with ASD. IEEE Trans Human-Mach Syst 48(2):125–135

    Article  Google Scholar 

  6. Unger H, Markert T, Müller E (2018) Evaluation of use cases of autonomous mobile robots in factory environments. Procedia Manuf 17:254–261

    Article  Google Scholar 

  7. Lundeen KM, Kamat VR, Menassa CC, Mcgee W (2019) Autonomous motion planning and task execution in geometrically adaptive robotized construction work. Autom Constr 100:24–45

    Article  Google Scholar 

  8. DaMota FA, Rocha MX, Rodrigues JJ, De Albuquerque VHC, De Alexandria AR (2018) Localization and navigation for autonomous mobile robots using Petri nets in indoor environments. IEEE Access 6:31665–31676

    Article  Google Scholar 

  9. De Almeida JPLS, Nakashima RT, Neves F, Arruda LVRD (2019) Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System. Robot Auton Syst 112:32–48

    Article  Google Scholar 

  10. Miao X, Lee J, Kang B-Y (2018) Scalable coverage path planning for cleaning robots using rectangular map decomposition on large environments. IEEE Access 6:38200–38215

    Article  Google Scholar 

  11. Diaz-Arango G, Vazquez-Leal H, Hernandez-Martinez L, Pascual MTS, Sandoval-Hernandez M (2018) Homotopy Path Planning for Terrestrial Robots Using Spherical Algorithm. IEEE Trans Autom Sci Eng 15(2):567–585

    Article  Google Scholar 

  12. Zhang H, Wang Y, Zheng J, Yu J (2018) Path planning of industrial robot based on improved RRT algorithm in complex environments. IEEE Access 6:53296–53306

    Article  Google Scholar 

  13. Drake D, Koziol S, Chabot E (2018) Mobile robot path planning with a moving goal. IEEE Access 6:12800–12814

    Article  Google Scholar 

  14. Zhe L, Yibin L, Xuewen R, Hui Z (2019) Path planning based on ADFA* algorithm for quadruped robot. IEEE Access 7:111095–111101

    Article  Google Scholar 

  15. Saeed R, Recupero DR, Remagnino P (2019) A Boundary Node Method for path planning of mobile robots. In: Robotics and autonomous systems, p 103320

  16. Low ES, Ong P, Cheah KC (2019) Solving the optimal path planning of a mobile robot using improved Q-learning. Rob Auton Syst 115:143–161

    Article  Google Scholar 

  17. Dewang HS, Mohanty PK, Kundu S (2018) A robust path planning for mobile robot using smart particle swarm optimization. Procedia Comput Sci 133:290–297

    Article  Google Scholar 

  18. Zhou Z, Wang J, Zhu Z, Yang D, Wu J (2018) Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik 158:639–651

    Article  Google Scholar 

  19. Andrade RCD, Saraiva RD (2018) An integer linear programming model for the constrained shortest path tour problem. Electron Notes Discrete Math 69:141–148

    Article  MathSciNet  Google Scholar 

  20. Chen M, Zhu D (2019) Real-time path planning for a robot to track a fast moving target based on improved Glasius bio-inspired neural networks. Int J Intell Rob Appl 3(2):186–195

    Article  Google Scholar 

  21. Wang X, Nie T, Zhu D (2019) Indoor robot path planning assisted by wireless network. EURASIP J Wirel Commun Network 1:2019

    Google Scholar 

  22. Adamu PI, Okagbue HI, Oguntunde PE (2019) Fast and optimal path planning algorithm (FAOPPA) for a mobile robot. Wireless Pers Commun 106(2):577–592

    Article  Google Scholar 

  23. Hassani I, Maalej I, Rekik C (2018) Robot path planning with avoiding obstacles in known environment using free segments and turning points algorithm. Math Problems Eng 2018:1–13

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. K. Arun.

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

Arun, K.K., Mydhili, S.K., Baskar, S. et al. Fuzzy rule-based environment-aware autonomous mobile robots for actuated touring. Intel Serv Robotics 15, 427–436 (2022). https://doi.org/10.1007/s11370-020-00320-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-020-00320-z

Keywords

Navigation