Skip to main content
Springer Nature Link
Log in

Dynamic path planning fusion algorithm with improved A* algorithm and dynamic window approach

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

In the field of robotics, path planning in complex dynamic environments has become a significant research hotspot. Existing methods often suffer from inadequate dynamic obstacle avoidance capabilities and low exploration efficiency. These issues primarily arise from inconsistencies caused by insufficient utilization of environmental maps in actual path planning. To address these challenges, we propose an improved algorithm that integrates the enhanced A* algorithm with the optimized dynamic window approach (DWA). The enhanced A* algorithm improves the robot’s path smoothness and accelerates global exploration efficiency, while the optimized DWA enhances local static and dynamic obstacle avoidance capabilities. We performed simulation experiments using MATLAB and conducted experiments in real dynamic environments simulated with Gazebo. Simulation results indicate that, compared to the traditional A* algorithm, our method optimizes traversed grids by 25% and reduces time by 23% in global planning. In dynamic obstacle avoidance, our approach improves path length by 2.7% and reduces time by 19.2% compared to the traditional DWA, demonstrating significant performance enhancements.

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
$34.99 /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 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
Algorithm 1
Algorithm 2
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Due to the datasets supporting this study are part of an ongoing project and will be available upon publication of related results or upon completion of patent application processes. The datasets generated and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

References

  1. Karaman S, Walter MR, Perez A, Frazzoli E, Teller S (2011) Anytime motion planning using the rrt. In: 2011 IEEE international conference on robotics and automation. IEEE, pp 1478–1483

  2. Kuffner JJ, LaValle SM (2000) Rrt-connect: an efficient approach to single-query path planning. In: Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposia proceedings (Cat. No. 00CH37065), vol 2. IEEE, pp 995–1001

  3. Fu J, Sun G, Liu J, Yao W, Wu L (2023) On hierarchical multi-uav dubins traveling salesman problem paths in a complex obstacle environment. IEEE Trans Cybern 54(1):123–35

    Article  MATH  Google Scholar 

  4. Fu J, Sun G, Yao W, Wu L (2022) On trajectory homotopy to explore and penetrate dynamically of multi-uav. IEEE Trans Intell Transport Sys 23(12):24008–24019

    Article  Google Scholar 

  5. Liu F, He H, Li Z, Guan Z-H, Wang HO (2020) Improved potential field method path planning based on genetic algorithm. In: 2020 39th Chinese control conference (CCC). IEEE, pp 3725–3729

  6. Jin Q, Tang C, Cai W (2021) Research on dynamic path planning based on the fusion algorithm of improved ant colony optimization and rolling window method. IEEE Access 10:28322–28332

    Article  MATH  Google Scholar 

  7. Yu N, Wang C, Mo F, Cai J (2017) Dynamic environment path planning based on q-learning algorithm and genetic algorithm. J Beijing Univ Technol 43(7):1009–1016

    MATH  Google Scholar 

  8. He M, Wei Z, Wu X, Peng Y (2021) An adaptive variable neighborhood search ant colony algorithm for vehicle routing problem with soft time windows. IEEE Access 9:21258–21266

    Article  MATH  Google Scholar 

  9. Han C, Li B (2023) Mobile robot path planning based on improved a* algorithm. In: 2023 IEEE 11th joint international information technology and artificial intelligence conference (ITAIC), vol 11. IEEE, pp 672–676

  10. Liu L-S, Lin J-F, Yao J-X, He D-W, Zheng J-S, Huang J, Shi P (2021) Path planning for smart car based on dijkstra algorithm and dynamic window approach. Wirel Commun Mob Comput 2021(1):8881684

    Article  MATH  Google Scholar 

  11. Paulino L, Hannum C, Varde AS, Conti CJ (2022) Search methods in motion planning for mobile robots. In: Intelligent systems and applications: proceedings of the 2021 intelligent systems conference (IntelliSys), vol 3. Springer, pp 802–822

  12. Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33

    Article  MATH  Google Scholar 

  13. Wu J, Ma X, Peng T, Wang H (2021) An improved timed elastic band (teb) algorithm of autonomous ground vehicle (agv) in complex environment. Sensors 21(24):8312

    Article  MATH  Google Scholar 

  14. Mizuno M, Kubota T (2020) A new path planning architecture to consider motion uncertainty in natural environment. In: 2020 IEEE international conference on robotics and automation (ICRA). IEEE, pp 2182–2188

  15. Fnadi M, Du W, Silva RG, Plumet F, Benamar F (2020) Local obstacle-skirting path planning for a fast bi-steerable rover using bézier curves. In: 2020 IEEE international conference on robotics and automation (ICRA). IEEE, pp 10242–10248

  16. Chang L, Shan L, Jiang C, Dai Y (2021) Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment. Autonom Robots 45:51–76

    Article  MATH  Google Scholar 

  17. Shao L, Li Q, Li C, Sun W (2021) Mobile robot path planning based on improved ant colony fusion dynamic window approach. In: 2021 IEEE international conference on mechatronics and automation (ICMA). IEEE, pp 1100–1105

  18. Yang L, Fu L, Li P, Mao J, Guo N (2022) An effective dynamic path planning approach for mobile robots based on ant colony fusion dynamic windows. Machines 10(1):50

    Article  MATH  Google Scholar 

  19. Sang H, You Y, Sun X, Zhou Y, Liu F (2021) The hybrid path planning algorithm based on improved a* and artificial potential field for unmanned surface vehicle formations. Ocean Eng 223:108709

    Article  Google Scholar 

  20. Liu L, Yao J, He D, Chen J, Huang J, Xu H, Wang B, Guo J (2021) Global dynamic path planning fusion algorithm combining jump-a* algorithm and dynamic window approach. IEEE Access 9:19632–19638

    Article  Google Scholar 

  21. Yonetani R, Taniai T, Barekatain M, Nishimura M, Kanezaki A (2021) Path planning using neural a* search. In: International conference on machine learning. PMLR, pp 12029–12039

  22. Wang J, Chi W, Li C, Wang C, Meng MQ-H (2020) Neural rrt*: learning-based optimal path planning. IEEE Trans Autom Sci Eng 17(4):1748–1758

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Natural Science Foundation of Fujian Provincial Science and Technology Department (No. 2021H6037), Quanzhou Science and Technology Project (No. 2021C0008R).

Author information

Authors and Affiliations

  1. School of Mathematics and Computer Science, Quanzhou Normal University, Donghai, Quanzhou, 362000, Fujian, China

    Jianfeng Zhang, Jielong Guo, Daxin Zhu & Yufang Xie

Authors
  1. Jianfeng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jielong Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Daxin Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yufang Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jielong Guo.

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

Zhang, J., Guo, J., Zhu, D. et al. Dynamic path planning fusion algorithm with improved A* algorithm and dynamic window approach. Int. J. Mach. Learn. & Cyber. 16, 2057–2071 (2025). https://doi.org/10.1007/s13042-024-02377-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-024-02377-z

Keywords