Skip to main content
SpringerLink
Log in

Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In order to solve the problems of the basic mayfly optimization algorithm (MOA) in the field of robot path planning, such as slow convergence speed, poor accuracy, insufficient stability, and only applicable to static environment, a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach is proposed in this paper. Firstly, an improved mayfly optimization algorithm based on Q-learning (IMOA-QL) is proposed to solve robot global path planning problem. Q-learning is taken as the core of the improved mayfly optimization algorithm. For the basic MOA, the inertia weight and positive attraction coefficients are set to fixed values, which are not reasonable and will make the global search ability unbalanced, fall into local optimization easily, and also limit the iteration speed. In this paper, the parameters are adaptively adjusted based on Q-learning, and the appropriate parameters are selected according to the fitness of each mayfly. Meanwhile, the memory mechanism is introduced to speed up the convergence speed and implement the global path planning. Then, the global path nodes are extracted as the sub-target points, and the improved dynamic window approach is used to carry out the local path planning, which effectively improves the dynamic real-time avoidance ability. In order to verify the effectiveness of the proposed IMOA-QL algorithm in this paper, 20 random simulation experiments are carried out in the 100 × 100 static map environment and compared with the basic mayfly optimization algorithm (MOA) and the mayfly optimization algorithm based on linear adaptive inertia weight (MOA-LAIW). The results show that the average path length of the proposed IMOA-QL algorithm is reduced by 4.48% and 2.17% compared with MOA and MOA-LAIW in simple environment, and the average path length of the proposed IMOA-QL algorithm is reduced by 6.58% and 3.24% compared with MOA and MOA-LAIW in complex environment. In 20 experiments, the average variance of the proposed IMOA-QL algorithm in this paper is reduced by 74.15% and 57.67% compared with MOA and MOA-LAIW in simple environment, and the average variance of the proposed IMOA-QL algorithm is reduced by 51.22% and 38.67% in complex environment compared with MOA and MOA-LAIW. The simulation results show that the proposed IMOA-QL algorithm has significantly improved the accuracy and speed of solution. Moreover, dynamic obstacles are added in the static environment to carry out the simulation test of the fusion dynamic path planning algorithm. The results show that a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach in this paper can better complete the path planning task well in the complex dynamic environment.

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

Similar content being viewed by others

Availability of data and materials

The data presented in this study are available on request from the corresponding authors.

References

  1. Zhang HY, Lin WM, Chen AX (2018) Path planning for the mobile robot: a review. Symmetry 10(10):1–17

    Article  Google Scholar 

  2. Akka K, Khaber F (2018) Mobile robot path planning using an improved ant colony optimization. Int J Adv Rob Syst 15(3):1–7

    Google Scholar 

  3. Chen X, Kong Y, Fang X, Wu Q (2013) A fast two-stage ACO algorithm for robotic path planning. Neural Comput Appl 3(22):313–319

    Article  Google Scholar 

  4. Orozco-Rosas U, Picos K, Montiel O (2019) Hybrid path planning algorithm based on membrane pseudo-bacterial potential field for autonomous mobile robots. IEEE Access 7:156787–156803

    Article  Google Scholar 

  5. Wahab MNA, Nefti-Meziani S, Atyabi A (2020) A comparative review on mobile robot path planning: Classical or meta-heuristic methods? Annu Rev Control 50:233–252

    Article  MathSciNet  Google Scholar 

  6. Shin Y, Kim E (2021) Hybrid path planning using positioning risk and artificial potential fields. Aerosp Sci Technol 112(6):1–14

    Google Scholar 

  7. Madhu SD, Sourish S, Sanjoy M (2022) Navigation of multiple robots in formative manner in an unknown environment using artificial potential field based path planning algorithm. Ain Shams Eng J 13(5):1–15

    Google Scholar 

  8. Wu X, Xu L, Zhen R, Wu X (2020) Bi-directional adaptive A* algorithm toward optimal path planning for large-scale UAV under multi-constraints. IEEE Access 8:85431–85440

    Article  Google Scholar 

  9. Tong X, Yu S, Liu G (2022) A hybrid formation path planning based on A* and multi-target improved artificial potential field algorithm in the 2D random environments. Adv Eng Inform. https://doi.org/10.1016/j.aei.2022.101755

    Article  Google Scholar 

  10. Li H, Zhao T, Dian S (2022) Prioritized planning algorithm for multi-robot collision avoidance based on artificial untraversable vertex. Appl Intell 52(1):429–451

    Article  Google Scholar 

  11. Elbanhawi M, Simic M (2014) Sampling-based robot motion planning: a review. IEEE Access 2:56–77

    Article  Google Scholar 

  12. Qie T, Wang W, Yang C (2022) A path planning algorithm for autonomous flying vehicles in cross-country environments with a novel TF-RRT* method. Green Energy Intell Transp. https://doi.org/10.1016/j.geits.2022.100026

    Article  Google Scholar 

  13. Noreen I, Khan A, Asghar K, Habib Z (2019) A path-planning performance comparison of RRT*-AB with MEA* in a 2-dimensional environment. Symmetry 11(7):1–16

    Article  Google Scholar 

  14. Hou WB, Xiong ZH, Wang CS, Chen H (2022) Enhanced ant colony algorithm with communication mechanism for mobile robot path planning. Robot Auton Syst 148:1–10

    Article  Google Scholar 

  15. Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53(3):2237–2264

    Article  Google Scholar 

  16. Zhang XY, Xia S, Li XZ, Zhang T (2022) Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles. Knowl Based Syst 250:1–19

    Article  Google Scholar 

  17. Xu L, Cao MY, Song BY (2022) A new approach to smooth path planning of mobile robot based on quartic Bezier transition curve and improved PSO algorithm. Neurocomputing 473:98–106

    Article  Google Scholar 

  18. Sathiya V, Chinnadurai M, Ramabalan S (2022) Mobile robot path planning using fuzzy enhanced improved multi-objective particle swarm optimization (FIMOPSO). Expert Syst Appl 198:1–24

    Article  Google Scholar 

  19. Nazarahari M, Khanmirza E, Doostie S (2019) Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst Appl 115:106–120

    Article  Google Scholar 

  20. Hao K, Zhao JL, Li ZS (2022) Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm. Ocean Eng 263:1–21

    Article  Google Scholar 

  21. Pehlivanoglu YV, Pehlivanoglu P (2021) An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems. Appl Soft Comput 112:1–18

    Article  Google Scholar 

  22. Qu CZ, Gai WD, Zhang J, Zhong MY (2020) A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowl Based Syst 194:1–14

    Article  Google Scholar 

  23. Faiza G, Imran M, Deemah A (2022) Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning. J Parallel Distrib Comput 169:171–184

    Article  Google Scholar 

  24. Mac TT, Copot C, Tran DT, De Keyser R (2016) Heuristic approaches in robot path planning: a survey. Robot Auton Syst 86:13–28

    Article  Google Scholar 

  25. Zeng Z, Sammut K, Lian HFP, Lammas A, Tang YH (2016) A comparison of optimization techniques for AUV path planning in environments with ocean currents. Robot Auton Syst 82:61–72

    Article  Google Scholar 

  26. Orozco-Rosas U, Montiel O, Sepulveda R (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Appl Soft Comput 77:236–251

    Article  Google Scholar 

  27. Orozco-Rosas U, Picos K, Pantrigo JJ (2022) Mobile robot path planning using a QAPF learning algorithm for known and unknown environments. IEEE Access 10:84648–84663

    Article  Google Scholar 

  28. Li HD, Zhao T, Dian SY (2022) Forward search optimization and subgoal-based hybrid path planning to shorten and smooth global path for mobile robots. Knowl Based Syst 258:1–10

    Article  Google Scholar 

  29. Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:1–23

    Article  Google Scholar 

  30. Gupta J, Nijhawan P, Ganguli S (2021) Parameter estimation of fuel cell using chaotic mayflies optimization algorithm. Adv Theory Simul 4(12):1–13

    Article  Google Scholar 

  31. Adnan RM, Kisi O, Mostafa RR, Ahmed AN, El-Shafie A (2022) The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction. Hydrol Sci J 67(2):161–174

    Article  Google Scholar 

  32. Liu Z, Jiang P, Wang J, Zhang L (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Syst Appl 177:1–22

    Article  Google Scholar 

  33. Wei D, Ji J, Fang J, Yousefi N (2021) Evaluation and optimization of PEM fuel cell-based CCHP system based on modified mayfly optimization algorithm. Energy Rep 7:7663–7674

    Article  Google Scholar 

  34. Guo L, Xu C, Yu TH, Wumaier T (2022) An improved mayfly optimization algorithm based on median position and its application in the optimization of PID parameters of hydro-turbine governor. IEEE Access 10:1–15

    Google Scholar 

  35. Indresh KG, Abha C, Siddhartha C (2022) Mayfly optimization with deep learning enabled retinal fundus image classification model. Comput Electr Eng 102:1–12

    Google Scholar 

  36. Yang HX, Teng XQ (2022) Mobile robot path planning based on enhanced dynamic window approach and improved A∗ algorithm. J Robot 2022:1–9

    Article  Google Scholar 

  37. Gao ZM, Zhao J, Li SR, Hu YR (2020) The improved mayfly optimization algorithm. J Phys Conf Ser 1684:1–7

    Article  Google Scholar 

  38. Thanh NH, Dieu TTD, Jaehong L (2021) Q-Learning-based parameter control in differential evolution for structural optimization. Appl Soft Comput 107:1–14

    Google Scholar 

  39. Sun RT, Yuan QN, Yi JH, Bao H (2022) Dynamic path planning by combing the improved particle swarm algorithm and dynamic window approach. J Chin Comput Syst. pp 1–10

  40. Wang B, Nie JJ, Li HY, Xie XL, Yan HZ (2022) Based on optimized A* and dynamic window approach for mobile robot path planning. Comput Integr Manuf Syst 2022:1–17

    Google Scholar 

  41. Jiang H, Sun Y (2021) Research on global path planning of electric disinfection vehicle based on improved A* algorithm. Energy Rep 7:1270–1279

    Article  Google Scholar 

Download references

Acknowledgements

Key Research Projects of Natural Science in Colleges and Universities of Anhui Province (2022AH050978). This paper was supported by the Anhui Province University Excellent Top Talent Training Project (gxbjZD2022023), Wuhu Science and Technology Project (2022jc26), Open Research Fund of Anhui Province Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University (JCKJ2021A06), Anhui Polytechnic University-Jiujiang District Industrial Collaborative Innovation Special Fund Project (2022cyxtb6, 2022cyxtb4), and Research Fund Project of Anhui Engineering University (2022YQQ002, Xjky2022002, Xjky2020001).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, 241000, Anhui, People’s Republic of China

    Awei Zou, Lei Wang, Weimin Li, Jingcao Cai & Hai Wang

  2. Wuhu Kepu Intelligent Equipment Co., Ltd, Wuhu, 241000, Anhui, People’s Republic of China

    Tielong Tan

Authors
  1. Awei Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weimin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingcao Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tielong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and formal analysis, Li W. M. and Zou A. W.; methodology and supervision, Wang L.; writing—original draft, Zou A. W. and Wang L.; writing—review and editing, Wang L and Cai J. C; Funding acquisition, Wang L.,Wang H., and Tan T. L. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Lei Wang or Jingcao Cai.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethics approval and consent to participate

No participation of humans takes place in this implementation process.

Human and animal rights

No violation of human and animal rights is involved.

Additional information

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

Zou, A., Wang, L., Li, W. et al. Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach. J Supercomput 79, 8340–8367 (2023). https://doi.org/10.1007/s11227-022-04998-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04998-z

Keywords

Search

Navigation