Skip to main content
SpringerLink
Log in

Robot path planning based on improved dung beetle optimizer algorithm

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

Abstract

In this paper, an improved dung beetle optimization algorithm (IDBO) combined with dynamic window approach (DWA) is proposed for the path planning problem in static and dynamic environments. This method mathematically models the rolling, breeding, foraging, and stealing behaviors of dung beetles. To address the constraints of the conventional dung beetle optimizer in path planning, four improvement facets are proposed to augment the algorithm's efficacy. First, to enhance the search randomness and diversity, an initial population initialization method using Chebyshev chaos map is introduced. Then, curve adaptive golden sine strategy (CGSS) is used to replace the rolling dung beetle position update formulation to increase convergence rate and accuracy during the search. Third, the position update formula for reproductive and foraging dung beetles was improved by using the Levy flights with Cauchy-t mutation strategy (LCTS) to increase the exploratory power and adaptability of the search. Finally, dynamic weight coefficient is introduced to adjust the stealing behavior formulation in order to improve the adaptability and robustness of the algorithm to different problems. The improved algorithm exhibits remarkable enhancements in search efficiency and solution quality by utilizing test functions and experimentally validating the path planning problem. Compared with the traditional dung beetle optimization algorithm with other optimization algorithms, the improved algorithm can converge to the optimal solution faster and has better global search capability and stability.

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

Similar content being viewed by others

References

  1. Zhang Y, Gao F, Zhao F (2023) Research on path planning and tracking control of autonomous vehicles based on improved RRT* and PSO-LQR. Processes 11(6):1841

    Article  Google Scholar 

  2. Rajmohan S, Ramasubramanian N (2023) Improved Symbiotic organisms search for path planning of unmanned combat aerial vehicles. J Ambient Intell Humaniz Comput 14(4):4289–4311

    Article  Google Scholar 

  3. Bahrami N, Siadatmousavi SM (2023) Ship voyage optimisation considering environmental forces using the iterative Dijkstra's algorithm. Sh Offshore Struct. https://doi.org/10.1080/17445302.2023.2231200

    Article  Google Scholar 

  4. Zhang Z, Jiang J, Wu J, Zhu X (2023) Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm. ISA Trans 134:42–57

    Article  Google Scholar 

  5. Fan J, Chen X, Liang X (2023) UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Expert Syst Appl 213:119137

    Article  Google Scholar 

  6. Luan PG, Thinh NT (2023) Hybrid genetic algorithm based smooth global-path planning for a mobile robot. Mech Based Des Struct Mach 51(3):1758–1774

    Article  Google Scholar 

  7. Huang C, Zhou X, Ran X, Wang J, Chen H, Deng W (2023) Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning. Eng Appl Artif Intell 121:105942

    Article  Google Scholar 

  8. Wu L, Huang X, Cui J, Liu C, Xiao W (2023) Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot. Expert Syst Appl 215:119410

    Article  Google Scholar 

  9. Xue J, Shen B (2023) Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305–7336

    Article  Google Scholar 

  10. Zhu F, Li G, Tang H, Li Y, Lv X, Wang X (2024) Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Syst Appl 236:121219

    Article  Google Scholar 

  11. Guo X, Qin X, Zhang Q, Zhang Y, Wang P, Fan Z (2023). Hyperparameter optimization of CNN using DBO for speaker recognition.

  12. Shen Q, Zhang D, Xie M, He Q (2023) Multi-strategy enhanced dung beetle optimizer and its application in three-dimensional UAV path planning. Symmetry 15(7):1432

    Article  Google Scholar 

  13. Dong Y, Yu Z, Hu T, He C (2023) Inversion of Rayleigh wave dispersion curve based on improved dung beetle optimizer algorithm. Pet Geol Recovery Effic 30(4):86–97

    Google Scholar 

  14. Chang Z, Luo J, Zhang Y, Teng Z (2023) A mixed strategy improved dung beetle optimization algorithm and its application

  15. Jin M, Wang H (2023) Robot path planning by integrating improved A* algorithm and DWA algorithm. J Phys Conf Ser 2492(1):012017

    Article  Google Scholar 

  16. Deebak BD, Hwang SO (2023) A cloud-assisted medical cyber-physical system using a privacy-preserving key agreement framework and a chebyshev chaotic map. IEEE Syst J 17(4):5543–5554

    Google Scholar 

  17. Li M, Xu G, Fu Y, Zhang T, Du L (2022) Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight. J Intell Fuzzy Syst 42(3):1501–1517

    Article  Google Scholar 

  18. Tanyildizi E, Demir G (2017) Golden sine algorithm: a novel math-inspired algorithm. Adv Electr Comput Eng 17(2):71–79

    Article  Google Scholar 

  19. Acikgoz H, Coteli R, Tanyildizi E, Dandil B, Kayisli K (2023) Advanced control of three-phase PWM rectifier using interval type-2 fuzzy neural network optimized by modified golden sine algorithm. Elect Power Compon Syst 51(10):933–948

    Article  Google Scholar 

  20. Zhao P, Zhang D, Zhang L, Zou C (2023) Bald eagle search optimization algorithm with golden sine algorithm and crisscross strategy. J Comput Appl 43(1):192

    Google Scholar 

  21. Lu W, Shi C, Fu H, Xu Y (2023) Fault diagnosis method for power transformers based on improved golden jackal optimization algorithm and random configuration network. IEEE Access 11(11):35336–35351

    Article  Google Scholar 

  22. He Q, Liu H, Ding G, Tu L (2023) A modified Lévy flight distribution for solving high-dimensional numerical optimization problems. Math Comput Simul 204:376–400

    Article  Google Scholar 

  23. Niu Y, Yan X, Wang Y, Niu Y (2023) Three-dimensional UCAV path planning using a novel modified artificial ecosystem optimizer. Expert Syst Appl 217:119499

    Article  Google Scholar 

  24. Liu X, Li G, Yang H, Zhang N, Wang L, Shao P (2023) Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm. Expert Syst Appl 233:120946

    Article  Google Scholar 

  25. Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S (2023) A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations. Arch Comput Methods Eng 30(7):4113–4159

    Article  Google Scholar 

  26. Sahoo SK, Saha AK, Ezugwu AE, Agushaka JO, Abuhaija B, Alsoud AR, Abualigah L (2023) Moth flame optimization: theory, modifications, hybridizations, and applications. Arch Comput Method Eng 30(1):391–426

    Article  Google Scholar 

  27. Nayak J, Swapnarekha H, Naik B, Dhiman G, Vimal S (2023) 25 years of particle swarm optimization: Flourishing voyage of two decades. Arch Comput Method Eng 30(3):1663–1725

    Article  Google Scholar 

  28. Alamir N, Kamel S, Megahed TF, Hori M, Abdelkader SM (2023) Developing hybrid demand response technique for energy management in microgrid based on pelican optimization algorithm. Elect Power Syst Res 214:108905

    Article  Google Scholar 

  29. Mohammed KK, Mekhilef S (2023) Improved snake optimizer algorithm-based GMPPT With a fast response to the load variations under different weather conditions for PV systems. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2023.3301526

    Article  Google Scholar 

  30. Xiong Q, She J, Xiong J (2023) A new pelican optimization algorithm for the parameter identification of memristive chaotic system. Symmetry 15(6):1279

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Faculty of Automation, Huaiyin Institute of Technology, Huaian, 223003, China

    He Jiachen & Fu Li-hui

Authors
  1. He Jiachen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fu Li-hui
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

[He Jiachen] was responsible for the design of the entire research project, data collection and analysis, interpretation of the results, and writing of the paper.

Corresponding author

Correspondence to Fu Li-hui.

Ethics declarations

Conflict of interests

The authors have no relevant financial or non-financial interests to disclose.

Consent to participate

Not Applicable.

Consent to publish

Not Applicable.

Ethics approval

Not Applicable.

Additional information

Technical Editor: 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

Jiachen, H., Li-hui, F. Robot path planning based on improved dung beetle optimizer algorithm. J Braz. Soc. Mech. Sci. Eng. 46, 235 (2024). https://doi.org/10.1007/s40430-024-04768-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-024-04768-3

Keywords

Search

Navigation