[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

Abstract

In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

References

  1. Patle BK, Ganesh BL, Anish P et al (2019) A review: on path planning strategies for navigation of mobile robot. Def Technol 15:582–606

    Article  Google Scholar 

  2. Gonzalez R, Kloetzer M, Mahulea C (2017) Comparative study of trajectories resulted from cell decomposition path planning approaches. In: 2017 21st international conference on system theory, control and computing, Sinaia, pp 49–54

  3. Zhang Z, Yang X (2019) Bio-inspired motion planning for reaching movement of a manipulator based on intrinsic tau jerk guidance. Adv Manuf 7:315–325

    Article  Google Scholar 

  4. Yang K, Tang Y, Zhang Z (2021) Parameter identification and state-of-charge estimation for lithium-ion batteries using separated time scales and extended Kalman filter. Energies 14(4):1054. https://doi.org/10.3390/en14041054

    Article  Google Scholar 

  5. Lee K, Choi D, Kim D (2021) Incorporation of potential fields and motion primitives for the collision avoidance of unmanned aircraft. Appl Sci Basel 11(7):3103. https://doi.org/10.3390/app11073103

    Article  Google Scholar 

  6. Guruji AK, Agarwal H, Parsediya DK (2016) Time-efficient A* algorithm for robot path planning. In: The 3rd international conference on innovations in automation and mechatronics engineering, Elsevier, Vallabh Vidhyanagar, pp 144–149

    Google Scholar 

  7. Chen C, Cai J, Wang Z et al (2020) An improved A* algorithm for searching the minimum dose path in nuclear facilities. Prog Nucl Energy 126:103394. https://doi.org/10.1016/j.pnucene.2020.103394

    Article  Google Scholar 

  8. Chen G, Luo N, Liu D et al (2021) Path planning for manipulators based on an improved probabilistic roadmap method. Robot Comput Integr Manuf 72:102196. https://doi.org/10.1016/j.rcim.2021.102196

    Article  Google Scholar 

  9. Sun Y, Zhang C, Sun P et al (2020) Safe and smooth motion planning for mecanum wheeled robot using improved RRT and cubic spline. Arab J Sci Eng 45:3075–3090

    Article  Google Scholar 

  10. Wu X, Xu L, Zhen R et al (2019) Biased sampling potentially guided intelligent bidirectional RRT algorithm for UAV path planning in 3D environment. Math Probl Eng 2019:5157403. https://doi.org/10.1155/2019/5157403

    Article  Google Scholar 

  11. Montiel O, Orozco-Rosas U, Sepúlveda R (2015) Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 42:5177–5191

    Article  Google Scholar 

  12. Jose K, Pratihar DK (2016) Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods. Robot Auton Syst 80:34–42

    Article  Google Scholar 

  13. Yan F, Liu YS, Xiao JZ (2013) Path planning in complex 3D environments using a probabilistic roadmap method. Int J Autom Comput 10:525–533

    Article  Google Scholar 

  14. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  15. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  16. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  17. Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8:22–34

    Article  Google Scholar 

  18. Xu R, Cao M, Huang M et al (2018) Research on the quasi-TSP problem based on the improved grey wolf optimization algorithm: a case study of tourism. Geogr Geo Inf Sci 34:14–21

    Google Scholar 

  19. Tian T, Liu C, Guo Q et al (2018) An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies 11:95. https://doi.org/10.3390/en11010095

    Article  Google Scholar 

  20. Yildiz AR (2019) A novel hybrid whale-Nelder-Mead algorithm for optimization of design and manufacturing problems. Int J Adv Manuf Technol 105:5091–5104

    Article  Google Scholar 

  21. Wang X, Shi H, Zhang C (2016) Path planning for intelligent parking system based on improved ant colony optimization. IEEE Access 8:65267–65273

    Article  Google Scholar 

  22. Niu H, Ji Z, Savvaris A et al (2020) Energy efficient path planning for nnmanned surface vehicle in spatially-temporally variant environment. Ocean Eng 196:106766. https://doi.org/10.1016/j.oceaneng.2019.106766

    Article  Google Scholar 

  23. Zhang C, Ding S (2021) A stochastic configuration network based on chaotic sparrow search algorithm. Knowl Based Syst 220:106924. https://doi.org/10.1016/j.knosys.2021.106924

  24. Liu G, Shu C, Liang Z et al (2021) A modified sparrow search algorithm with application in 3D route planning for UAV. Sensors 21:1224. https://doi.org/10.3390/s21041224

    Article  Google Scholar 

  25. Raouf F, Mohammed B, Tamer R et al (2020) Enhancing path quality of real-time path planning algorithms for mobile robots: a sequential linear paths approach. IEEE Access 8:167090–167104

    Article  Google Scholar 

  26. Ajeil FH, Ibraheem KI, Sahib MA et al (2018) Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Appl Soft Comput 89:106076. https://doi.org/10.1016/j.asoc.2020.106076

    Article  Google Scholar 

  27. Li X, Huang Y, Zhou Y et al (2018) Robot path planning using improved artificial bee colony algorithm. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference, Chongqing, China, pp 603–607

  28. Zhang D, You X, Liu S et al (2020) Dynamic multi-role adaptive collaborative ant colony optimization for robot path planning. IEEE Access 8:129958–129974

    Article  Google Scholar 

  29. Zinage V, Ghosh S (2020) Directional sampling-based generalized shape expansion for accelerated motion planning in 2-D obstacle-cluttered environments. IEEE Contr Syst Lett 5:1067–1072

    Article  MathSciNet  Google Scholar 

  30. Huang Y, Li Z, Jiang Y et al (2019) Cooperative path planning for multiple mobile robots via HAFSA and an expansion logic strategy. Appl Sci Basel 9:672. https://doi.org/10.3390/app9040672

    Article  Google Scholar 

  31. Alaa T, Mohamed E, Aboul EH et al (2019) Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Comput 22:4745–4766

    Article  Google Scholar 

  32. Hassani I, Maalej I, Rekik C (2018) Robot path planning with avoiding obstacles in known environment using free segments and turning points algorithm. Math Probl Eng 2018:2163278. https://doi.org/10.1155/2018/2163278

    Article  Google Scholar 

  33. Wang Z, Xiang X (2018) Improved A star algorithm for path planning of marine robot. In: 2018 37th Chinese control conference. IEEE, Wuhan, China, pp 5410–5414

Download references

Acknowledgements

This research was jointly supported by the National Key R&D Program of China (Grant No. 2018YFB1309200) and the Opening Project of Shanghai Robot Industry R&D and Transformation Functional Platform.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, People’s Republic of China

    Zhen Zhang, Rui He & Kuo Yang

Authors
  1. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kuo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., He, R. & Yang, K. A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm. Adv. Manuf. 10, 114–130 (2022). https://doi.org/10.1007/s40436-021-00366-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-021-00366-x

Keywords

Search

Navigation