Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

ERCP: speedup path planning through clustering and presearching

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Path planning is an important task for robot motion, unmanned aerial vehicle obstacle avoidance, and smart cars. Sampling-based algorithms have achieved significant success in this task. The rapidly random-exploring tree (RRT) is one of the representative algorithms. However, it is exceedingly difficult to strike a balance between path quality and search efficiency. In this paper, we propose an enhanced RRT* with clustering and presearching (ERCP) algorithm to handle this issue. In the clustering phase, we construct an 8-degree undirected graph according to the neighborhood and obstacles. Then, we adopt the Markov clustering technique, which is appropriate for spatial data. The clustering process is efficient since the coarse-grained map considers only points at integer locations. In the presearching phase, we utilize the clustering results to abstract the intact state space as an undirected graph. We employ Dijkstra’s algorithm to perform a presearch on the graph to determine the effective sampling area. In the path planning phase, we use RRT* to extend the space-filling tree within the effective sampling regions. Experiments were conducted on ten maps with different levels of obstacle complexity. The results reveal that ERCP can achieve a more convincing balance between path quality and search efficiency than the three state-of-the-art algorithms with little sacrifice.

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
Algorithm 1
Algorithm 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

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

Data Availability

Data will be available upon reasonable request.

References

  1. Siciliano B, Khatib O, Kröger T (2008) Springer handbook of robotics, vol 200. Springer, New York

    Book  Google Scholar 

  2. Liu Y, Xiao F, Tong X, Tao B, Xu M, Jiang G, Chen B, Cao Y, Sun N (2022) Manipulator trajectory planning based on work subspace division. Concurr Comput Pract Experience 34(5):e6710

    Article  Google Scholar 

  3. Ab Wahab MN, Nefti-Meziani S, Atyabi A (2020) A comparative review on mobile robot path planning: classical or meta-heuristic methods? Ann Rev Control 50:233–252

    Article  MathSciNet  Google Scholar 

  4. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybernet 4(2):100–107

    Article  Google Scholar 

  5. Stentz A (1997) Optimal and efficient path planning for partially known environments. In: Intelligent unmanned ground vehicles, Springer, pp 203–220

  6. Lingelbach F (2004) Path planning using probabilistic cell decomposition. In: IEEE international conference on robotics and automation, vol 1. IEEE, pp 467–472

  7. Li B, Liu H, Su W (2019) Topology optimization techniques for mobile robot path planning. Appl Soft Comput 78:528–544

    Article  Google Scholar 

  8. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: IEEE international conference on robotics and automation, vol 2. pp 500–505

  9. Luo Q, Wang H, Zheng Y, He J (2020) Research on path planning of mobile robot based on improved ant colony algorithm. Neural Comput Applic 32(6):1555–1566

    Article  Google Scholar 

  10. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics. computational cybernetics and simulation. vol 5. pp 4104–4108

  11. Shi K, Huang L, Jiang D, Sun Y, Tong X, Xie Y, Fang Z (2022) Path planning optimization of intelligent vehicle based on improved genetic and ant colony hybrid algorithm. Front Bioeng Biotechnol 10:905983

    Article  Google Scholar 

  12. Zhang X, Xiao F, Tong X, Yun J, Liu Y, Sun Y, Tao B, Kong J, Xu M, Chen B (2022) Time optimal trajectory planing based on improved sparrow search algorithm. Front Bioeng Biotechnol 10:852408

    Article  Google Scholar 

  13. Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894

    Article  MATH  Google Scholar 

  14. Hsu D, Latombe JC, Motwani R (1997) Path planning in expansive configuration spaces. In: Proceedings of international conference on robotics and automation. vol 3. pp 2719–2726

  15. Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580

    Article  Google Scholar 

  16. LaValle SM (1998) Rapidly-Exploring Random Trees: A New Tool for Path Planning. Technical Report. pp 98–11

  17. Dongen V, Marinus S (2000) Graph Clustering by Flow Simulation

  18. Gammell JD, Srinivasa SS, Barfoot TD (2014) Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: IEEE/RSJ international conference on intelligent robots and systems. pp 2997–3004

  19. Islam F, Nasir J, Malik U, Ayaz Y, Hasan O (2012) RRT*-Smart: rapid convergence implementation of RRT* towards optimal solution. In: IEEE international conference on mechatronics and automation. pp 1651–1656

  20. Wang J, Li B, Meng MQH (2021) Kinematic constrained bi-directional RRT with efficient branch pruning for robot path planning. Expert Syst Appl 170:114541

    Article  Google Scholar 

  21. Chen L, Shan Y, Tian W, Li B, Cao D (2018) A fast and efficient double-tree RRT∗-like sampling-based planner applying on mobile robotic systems. IEEE/ASME Trans Mechatron 23 (6):2568–2578

    Article  Google Scholar 

  22. Qi J, Yang H, Sun H (2021) MOD-RRT*: a sampling-based algorithm for robot path planning in dynamic environment. IEEE Trans Ind Electron 68(8):7244–7251

    Article  Google Scholar 

  23. Qureshi AH, Ayaz Y (2016) Potential functions based sampling heuristic for optimal path planning. Auton Robot 40(6):1079–1093

    Article  Google Scholar 

  24. Jeong IB, Lee SJ, Kim JH (2019) Quick-RRT*: triangular inequality-based implementation of RRT* with improved initial solution and convergence rate. Expert Syst Appl 123:82–90

    Article  Google Scholar 

  25. Li Y, Wei W, Gao Y, Wang D, Fan Z (2020) PQ-RRT*: an improved path planning algorithm for mobile robots. Expert Syst Appl 152:113425

    Article  Google Scholar 

  26. Dong Y, Camci E, Kayacan E (2018) Faster RRT-based nonholonomic path planning in 2D building environments using skeleton-constrained path biasing. J Intell Robot Syst 89(3):387–401

    Article  Google Scholar 

  27. Wang J, Li T, Li B, Meng MQH (2022) GMR-RRT*: sampling-based path planning using Gaussian mixture regression. IEEE Trans Intell Veh

  28. Li Y, Cui R, Li Z, Xu D (2018) Neural network approximation based Near-Optimal motion planning with kinodynamic constraints using RRT. IEEE Trans Ind Electron 65(11):8718–8729

    Article  Google Scholar 

  29. Mohammadi M, Al-Fuqaha A, Oh JS (2018) Path planning in support of smart mobility applications using generative adversarial networks. In: IEEE international conference on internet of things and IEEE green computing and communications and IEEE cyber, physical and social computing and IEEE smart data. pp 878–885

  30. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Conference and workshop on neural information processing systems

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

    Article  Google Scholar 

  32. Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang W, Zuo L, Xu X (2018) A learning-based multi-RRT approach for robot path planning in narrow passages. J Intell Robot Syst 90(1):81–100

    Article  Google Scholar 

Download references

Acknowledgements

This work is in part supported by the Central Government Funds of Guiding Local Scientific and Technological Development (No. 2021ZYD0003), the Sichuan Province Youth Science and Technology Innovation Team (No. 2019JDTD0017), the Science and Technology Planning Project of Sichuan Province (No. 2021YFS0391), and the Scientific Research Project of State Grid Sichuan Electric Power Company Information and Communication Company (No. SGSCXT00XGJS2100116).

Author information

Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, People’s Republic of China

    Kun He & Fan Min

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610500, People’s Republic of China

    Xin-Zheng Niu

  3. School of Sciences, Southwest Petroleum University, Chengdu, 610500, People’s Republic of China

    Xue-Yang Min

Authors
  1. Kun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin-Zheng Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xue-Yang Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fan Min
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fan Min.

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 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

He, K., Niu, XZ., Min, XY. et al. ERCP: speedup path planning through clustering and presearching. Appl Intell 53, 12324–12339 (2023). https://doi.org/10.1007/s10489-022-04137-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04137-4

Keywords

Search

Navigation