Skip to main content
SpringerLink
Log in

Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

With the evolution of the industrial processes, mobile robots have gained a considerable interest due to their ability to perform rapidly and efficiently complex tasks, notably the dangerous ones. Hence, designing a safe path and controlling the motion of mobile robot is a prerequisite requirement for any such mobile robot mission. This paper employs firefly algorithm (FA) to solve the path planning problem. The developed algorithm engages a population of fireflies to find a shortest trajectory without any physical meeting with obstacles from the starting point to the destination based on their brightness. FA covers two kinds of objectives which are the path length and the path safety. In order to test the efficiency of the proposed algorithm, a comparison with particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) was made. Furthermore, a kinematic controller combined with sliding mode control (SMC) approach is proposed to follow the obtained path by FA. The kinematic controller was developed to provide the required velocities, whereas two SMCs are adopted for velocity tracking and steering control of mobile robot. In order to attain its best performance, the FA algorithm was also devoted to find its best parameters. To verify its feasibility, two well-known reference trajectories (circle and 8 shape) were considered. Finally, the involved results in this work indicate that FA outperforms other algorithms, which can be used as a path planar to generate an adequate trajectory of mobile robot and demonstrate that the designed controller provides height tracking ability.

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
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Pattanayak S, Agarwal S, Choudhury BB, Sahoo SC (2019) Path planning of mobile robot using PSO algorithm. Information and communication technology for intelligent systems. Springer, Singapore, pp 515–522

    Chapter  Google Scholar 

  2. Patle BK, Pandey A, Jagadeesh A, Parhi DR (2018) Path planning in uncertain environment by using firefly algorithm. Def Technol 14(6):691–701

    Article  Google Scholar 

  3. Sung I, Choi B, Nielsen P (2021) On the training of a neural network for online path planning with offline path planning algorithms. Int J Inf Manag 57:102142. https://doi.org/10.1016/j.ijinfomgt.2020.102142

    Article  Google Scholar 

  4. Mohanty PK, Kundu S, Srivastava S, Dash RN (2020) A new TS model based fuzzy logic approach for mobile robots path planning. In: IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEE, pp 476–480. https://doi.org/10.1109/WIECON-ECE52138.2020.9397986

  5. Yao Q, Zheng Z, Qi L, Yuan H, Guo X, Zhao M, Yang T (2020) Path planning method with improved artificial potential field—a reinforcement learning perspective. IEEE Access 8:135513–135523. https://doi.org/10.1109/ACCESS.2020.3011211

    Article  Google Scholar 

  6. Sang H, You Y, Sun X, Zhou Y, Liu F (2020) The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations. Ocean Eng 223:108709. https://doi.org/10.1016/j.oceaneng.2021.108709

    Article  Google Scholar 

  7. Liu J, Anavatti S, Garratt M, Abbass HA (2022) Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning. Expert Syst Appl 196:116605. https://doi.org/10.1016/j.eswa.2022.116605

    Article  Google Scholar 

  8. Guo J, Li C, Guo S (2019) A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control. IEEE Access 8:1394–1405. https://doi.org/10.1109/ACCESS.2019.2962074

    Article  Google Scholar 

  9. Yao Q, Tian Y, Wang Q, Wang S (2020) Control strategies on path tracking for autonomous vehicle: state of the art and future challenges. IEEE Access 8:161211–161222. https://doi.org/10.1109/ACCESS.2020.3020075

    Article  Google Scholar 

  10. Xu T, Ji X, Liu Y, Liu Y (2020) Differential drive based yaw stabilization using MPC for distributed-drive articulated heavy vehicle. IEEE Access 8:104052–104062. https://doi.org/10.1109/ACCESS.2020.2998510

    Article  Google Scholar 

  11. Li J, Wang J, Wang S, Qi W, Zhang L, Hu Y, Su H (2021) Neural approximation-based model predictive tracking control of non-holonomic wheel-legged robots. Int J Control Autom Syst 19(1):372–381. https://doi.org/10.1007/s12555-019-0927-2

    Article  Google Scholar 

  12. Nath K, Yesmin A, Nanda A, Bera MK (2021) Event-triggered sliding-mode control of two wheeled mobile robot: an experimental validation. IEEE J Emerg Sel Top Ind Electron 2(3):218–226. https://doi.org/10.1109/JESTIE.2021.3087965

    Article  Google Scholar 

  13. Panahandeh P, Alipour K, Tarvirdizadeh B, Hadi A (2019) A self-tuning trajectory tracking controller for wheeled mobile robots. Ind Robot 46(6):828–838

    Article  Google Scholar 

  14. Achouri M, Zennir Y, Tolba C (2022) Intelligent and robust controller tuned with WOA: applied for the inverted pendulum. J Eur des Syst Autom 55(3):359–366

    Google Scholar 

  15. Mourad A, Youcef Z (2022) Fuzzy-PI controller tuned with HBBO for 2 DOF robot trajectory control. Eng Proc 14(1):10. https://doi.org/10.3390/engproc2022014010

    Article  Google Scholar 

  16. Mourad A, Youcef Z (2022) Fuzzy-PI controller tuned with ICA: applied to 2 DOF robot control trajectory. In: IEEE International Conference on Information, Communication and Automation Technologies. IEE, pp 1–6. https://doi.org/10.1109/ICAT54566.2022.9811113.

  17. Achouri M, Zennir Y, Tolba C (2022) Fuzzy-PI controller tuned With GWO, WOA and TLBO for 2 DOF robot trajectory control. Alger J Signals Syst 7(1):1–6. https://doi.org/10.51485/ajss.v7i1.150

    Article  Google Scholar 

  18. Štefek A, Pham VT, Krivanek V, Pham KL (2021) Optimization of fuzzy logic controller used for a differential drive wheeled Mobile robot. Appl Sci 11(13):6023. https://doi.org/10.3390/app11136023

    Article  Google Scholar 

  19. Serrano-Pérez O, Villarreal-Cervantes MG, González-Robles JC, Rodríguez-Molina A (2019) Meta-heuristic algorithms for the control tuning of omnidirectional mobile robots. Eng Optim 52(2):325–342. https://doi.org/10.1080/0305215X.2019.1585834

    Article  MathSciNet  Google Scholar 

  20. Zhang L, Liu L, Zhang S (2020) Design, implementation, and validation of robust fractional-order pd controller for wheeled mobile robot trajectory tracking. Complexity 2020:1–12. https://doi.org/10.1155/2020/9523549

    Article  Google Scholar 

  21. Singhal K, Kumar V, Rana KPS (2022) Robust trajectory tracking control of non-holonomic wheeled mobile robots using an adaptive fractional order parallel fuzzy PID controller. J Franklin Inst 359(9):4160–4215. https://doi.org/10.1016/j.jfranklin.2022.03.043

    Article  MathSciNet  Google Scholar 

  22. Abed AM, Rashid ZN, Abedi F, Zeebaree SR, Sahib MA, Mohamad Jawad AJA, Al-khaykan A (2022) Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization. Meas Control 55(3–4):209–226. https://doi.org/10.1177/00202940221092134

    Article  Google Scholar 

  23. Lee K, Im DY, Kwak B, Ryoo YJ (2018) Design of fuzzy-PID controller for path tracking of mobile robot with differential drive. Int J Fuzzy Logic Intell Syst 18(3):220–228. https://doi.org/10.5391/IJFIS.2018.18.3.220

    Article  Google Scholar 

  24. Xu L, Du J, Song B, Cao M (2022) A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots. Syst Sci Control Eng 10(1):134–141. https://doi.org/10.1080/21642583.2022.2047125

    Article  Google Scholar 

  25. Precup RE, David RC, Roman RC, Szedlak-Stinean AI, Petriu EM (2021) Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm. Int J Syst Sci. https://doi.org/10.1080/00207721.2021.1927236

    Article  Google Scholar 

  26. Khai TQ, Ryoo YJ, Gill WR, Im DY (2020) Design of kinematic controller based on parameter tuning by fuzzy inference system for trajectory tracking of differential-drive mobile robot. Int J Fuzzy Syst 22:1972–1978

    Article  Google Scholar 

  27. Mai TA, Dang TS, Duong DT, Le VC, Banerjee S (2021) A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots. J Braz Soc Mech Sci Eng 43:1–13

    Article  Google Scholar 

  28. Martins FN, Sarcinelli-Filho M, Carelli R (2017) A velocity-based dynamic model and its properties for differential drive mobile robots. J Intell Rob Syst 85:277–292

    Article  Google Scholar 

  29. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications, SAGA 2009 Lecture Notes in Computer Science, vol 5792. Springer, Berlin

    Google Scholar 

  30. Li F, Fan X, Hou ZA (2020) firefly algorithm with self-adaptive population size for global path planning of mobile robot. IEEE Access 8:168951–168964. https://doi.org/10.1109/ACCESS.2020.3023999

    Article  Google Scholar 

  31. Moshayedi AJ, Abbasi A, Liao L, Li S (2019) Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control. In: 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp 1–6. IEEE

  32. Mourad A, Youcef Z (2022) Wheeled mobile robot path planning and path tracking in a static environment using TLBO AND PID-TLBO control. In: 2022 IEEE 21st international Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp 116–121. IEEE

  33. Mourad, Youcef Z (2022) Fuzzy adaptive sliding mode controller: applied to inverted pendulum. In: 2022 IEEE 21st international Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp 28–33. IEEE

  34. Mourad A, Youcef Z (2022) Adaptive sliding mode control improved by Fuzzy-PI controller: applied to magnetic levitation system. Eng Proc 14(1):14

    Google Scholar 

  35. Moudoud B, Aissaoui H, Diany M (2022) Fuzzy adaptive sliding mode controller for electrically driven wheeled mobile robot for trajectory tracking task. J Control Decis 9(1):71–79. https://doi.org/10.1080/23307706.2021.1912665

    Article  MathSciNet  Google Scholar 

  36. Li J, Wang J, Peng H, Hu Y, Su H (2021) Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot. IEEE Trans Syst Man Cybern Syst 52(4):2491–2500. https://doi.org/10.1109/TSMC.2021.3050616

    Article  Google Scholar 

  37. Azzabi A, Nouri K (2021) Design of a robust tracking controller for a nonholonomic mobile robot based on sliding mode with adaptive gain. Int J Adv Rob Syst 18(1):1729881420987082. https://doi.org/10.1177/172988142098708

    Article  Google Scholar 

  38. Larabi MS, Yahmedi S, Zennir Y (2022) Robust LQG controller design by LMI approach of a doubly-fed induction generator for aero-generator. J Eur des Syst Autom 55(6):803. https://doi.org/10.18280/jesa.550613

    Article  Google Scholar 

  39. Wu X, Jin P, Zou T, Qi Z, Xiao H, Lou P (2019) Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots. J Intell Rob Syst 96:109–121

    Article  Google Scholar 

  40. Qin M, Dian S, Guo B, Tao X, Zhao T (2022) Fractional-order SMC controller for mobile robot trajectory tracking under actuator fault. Syst Sci Control Eng 10(1):312–324

    Article  Google Scholar 

  41. Li THS, Huang YC (2010) MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators. Inf Sci 180(23):4641–4660

    Article  MathSciNet  Google Scholar 

  42. De La Cruz C, Carelli R (2008) Dynamic model based formation control and obstacle avoidance of multi-robot systems. Robotica 26(3):345–356. https://doi.org/10.1017/S0263574707004092

    Article  Google Scholar 

  43. Gheisarnejad M, Khooban MH (2020) An intelligent non-integer PID controller-based deep reinforcement learning: implementation and experimental results. IEEE Trans Ind Electron 68(4):3609–3618. https://doi.org/10.1109/TIE.2020.2979561

    Article  Google Scholar 

  44. Khai TQ, Ryoo YJ (2019) Design of adaptive kinematic controller using radial basis function neural network for trajectory tracking control of differential-drive mobile robot. Int J Fuzzy Log Intell Syst 19(4):349–359. https://doi.org/10.5391/IJFIS.2019.19.4.349

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LRPCSI Laboratory Skikda, Université 20 Aout 1955, 21000, Skikda, Algeria

    Mourad Achouri

  2. Automatic Laboratory of Skikda, Université 20 Août 1955, 21000, Skikda, Algeria

    Youcef Zennir

Authors
  1. Mourad Achouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youcef Zennir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youcef Zennir.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Consent for publication

We confirm that this paper has not been published elsewhere.

Additional information

Technical Editor by Rogério Sales Gonçalves.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Achouri, M., Zennir, Y. Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control. J Braz. Soc. Mech. Sci. Eng. 46, 228 (2024). https://doi.org/10.1007/s40430-024-04772-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-024-04772-7

Keywords

Search

Navigation