Skip to main content
SpringerLink
Log in

An enhanced heuristic ant colony optimization for mobile robot path planning

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

To realize a fast and efficient path planning for mobile robot in complex environment, an enhanced heuristic ant colony optimization (EH-ACO) algorithm is proposed. Four strategies are introduced to accelerate the ACO algorithm and optimize the final path. Firstly, the heuristic distance in the local visibility formula is improved by considering the heuristic distance from ant’s neighbor points to target. Secondly, a new pheromone diffusion gradient formula is designed, which emphasizes that pheromones left the path would spread into a region and the pheromone density would present a gradient distribution in the region. Thirdly, backtracking strategy is introduced to enable ants to find new path when their search is blocked. Finally, path merging strategy is designed to further obtain an optimal path. Simulations are carried out to verify each individual strategy, and comparisons are made with the state-of-the-art algorithms. The results show our proposed EH-ACO algorithm outperforms other algorithms in both optimality and efficiency, especially when the map is large and complex.

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

Similar content being viewed by others

References

  • Benhala B, Kotti M, Ahaitouf A, Fakhfakh M (2015) Backtracking ACO for RF-circuit design optimization. In: Performance optimization techniques in analog, mixed-signal, and radio-frequency circuit design. IGI Global, pp 158–179

  • Cekmez U, Ozsiginan M, Sahingoz OK (2016) Multi colony ant optimization for UAV path planning with obstacle avoidance. In: International conference on unmanned aircraft systems (ICUAS). IEEE, pp 47–52

  • Chen G, Liu J (2019) Mobile robot path planning using ant colony algorithm and improved potential field method. Comput Intell Neurosci. https://doi.org/10.1155/2019/1932812

    Article  Google Scholar 

  • Chen X, Kong Y, Fang X, Wu Q (2013) A fast two-stage ACO algorithm for robotic path planning. Neural Comput Appl 22(2):313–319

    Article  Google Scholar 

  • Deneubourg JL, Clip PL, Camazine SS (1994) Ants, buses and robots-self-organization of transportation systems. In: Proceedings from perception to action conference. IEEE, pp 12–23

  • Deng X, Zhang L, Lin H, Luo L (2015) Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy. Neurocomputing 148:46–53

    Article  Google Scholar 

  • Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE, pp 1470–1477

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41

    Article  Google Scholar 

  • Friudenberg P, Koziol S (2018) Mobile robot rendezvous using potential fields combined with parallel navigation. IEEE Access 6:16948–16957

    Article  Google Scholar 

  • Garcia MP, Montiel O, Castillo O, Sepúlveda R, Melin P (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 9(3):1102–1110

    Article  Google Scholar 

  • Gigras Y, Choudhary K, Gupta K, et al (2015) A hybrid ACO-PSO technique for path planning. In: 2nd International conference on computing for sustainable global development (INDIACom). IEEE, pp 1616–1621

  • Habib N, Purwanto D, Soeprijanto A (2016) Mobile robot motion planning by point to point based on modified ant colony optimization and voronoi diagram. In: International seminar on intelligent technology and its applications (ISITIA). IEEE, pp 613–618

  • Heegaard PE, Wittner OJ (2006) Restoration performance vs. overhead in a swarm intelligence path management system. In: International workshop on ant colony optimization and swarm intelligence. Springer, pp 282–293

  • Kumar PB, Sahu C, Parhi DR (2018) A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment. Appl Soft Comput 68:565–585

    Article  Google Scholar 

  • Lee ZJ, Su SF, Chuang CC, Liu KH (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput 8(1):55–78

    Article  Google Scholar 

  • Li H, Savkin AV (2018) An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments. Robot Comput Integr Manuf 54:65–82

    Article  Google Scholar 

  • Li X, Tian P (2006) An ant colony system for the open vehicle routing problem. In: International workshop on ant colony optimization and swarm intelligence. Springer, pp 356–363

  • Liu J, Yang J, Liu H, Tian X, Gao M (2017) An improved ant colony algorithm for robot path planning. Soft Comput 21(19):5829–5839

    Article  Google Scholar 

  • Luo RC, Hsiao TJ (2019) Dynamic wireless indoor localization incorporating with an autonomous mobile robot based on an adaptive signal model fingerprinting approach. IEEE Trans Ind Electron 66(3):1940–1951

    Article  Google Scholar 

  • 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 

  • Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  • Miah MS, Knoll J, Hevrdejs K (2018) Intelligent range-only mapping and navigation for mobile robots. IEEE Trans Ind Inform 14(3):1164–1174

    Article  Google Scholar 

  • Mou C, Qing-xian W, Chang-sheng J (2008) A modified ant optimization algorithm for path planning of UCAV. Appl Soft Comput 8(4):1712–1718

    Article  Google Scholar 

  • Patle B, Parhi D, Jagadeesh A, Kashyap SK (2019) Application of probability to enhance the performance of fuzzy based mobile robot navigation. Appl Soft Comput 75:265–283

    Article  Google Scholar 

  • Seçkiner SU, Eroğlu Y, Emrullah M, Dereli T (2013) Ant colony optimization for continuous functions by using novel pheromone updating. Appl Math Comput 219(9):4163–4175

    MathSciNet  MATH  Google Scholar 

  • Yang J, Zhuang Y (2010) An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl Soft Comput 10(2):653–660

    Article  Google Scholar 

  • Yu L, Wei Z, Wang H, Ding Y, Wang Z (2017) Path planning for mobile robot based on fast convergence ant colony algorithm. In: IEEE international conference on mechatronics and automation (ICMA). IEEE, pp 1493–1497

  • Yuan J, Wang H, Lin C, Liu D, Yu D (2019) A novel GRU-RNN network model for dynamic path planning of mobile robot. IEEE Access 7:15140–15151

    Article  Google Scholar 

  • Zhu Q, Wang L (2008) A new algorithm for robot path planning based on scout ant cooperation. In: Fourth international conference on natural computation, ICNC’08, vol 7. IEEE, pp 444–449

  • Zhu Q, Hu J, Cai W, Henschen L (2011) A new robot navigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm. Appl Soft Comput 11(8):4667–4676

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation (NNSF) of China under 61503265 and the Sichuan Science and Technology Program under Grant 2017SZ0096.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Sichuan University, Chengdu, 610065, China

    Wenxiang Gao, Qing Tang, Beifa Ye, Yaru Yang & Jin Yao

Authors
  1. Wenxiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beifa Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaru Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Tang.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by A. Di Nola.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, W., Tang, Q., Ye, B. et al. An enhanced heuristic ant colony optimization for mobile robot path planning. Soft Comput 24, 6139–6150 (2020). https://doi.org/10.1007/s00500-020-04749-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04749-3

Keywords

Search

Navigation