Skip to main content
Springer Nature Link
Log in

A novel path planning algorithm based on plant growth mechanism

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

We propose a bio-inspired computing algorithm based on plant growth mechanism and describe its application in path planning in this paper. The basic rules of the algorithm include phototropism, negative geotropism, apical dominance, and branch in plant growth. The starting point of the algorithm is the seed germ (first bud) and the target point of the algorithm is the light source. The discretization of the plant growth process is used to realize computation in computer. The plant growth behavior in each iteration is assumed to be the same. The algorithm includes six steps: initialization, light intensity calculation, random branch, growth vector calculation, plant growth and path output. Several two-dimensional path planning problems are used to validate the algorithm. The test results show that the algorithm has good path planning ability and provides a novel path planning approach.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

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

References

  • Alejandro HP, Miguel AVR, Joaquin F (2015) MOSFLA-MRPP: multi-objective shuffled frog-leaping algorithm applied to mobile robot path planning. Eng Appl Artif Intell 44:123–136

    Article  Google Scholar 

  • Atsushi T, Ryo K, Toshiyuki N (2006) Physarum solver: a biologically inspired method of road-network navigation. Phys A Stat Mech Appl 363(1):115–119

    Article  Google Scholar 

  • Atsushi T, Ryo K, Toshiyuki N (2007) A mathematical model for adaptive transport network in path finding by true slime mold. J Theor Biol 244(4):533–564

    MathSciNet  Google Scholar 

  • Aydin S, Temeltas H (2004) Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path. Adv Robot 18(7):725–748

    Article  Google Scholar 

  • Bayat FM (2014) A numerical optimization algorithm inspired by the strawberry plant. Eprint Arxiv

  • Bhattacharjee P, Rakshit P, Goswami I, Konar A, Nagar AK (2011) Multi-robot path-planning using artificial bee colony optimization algorithm. In: Proceedings of third world congress on nature and biologically inspired computing, pp 219–224, 2011

  • Chen M, Wu QX, Jiang CS (2008) A modified ant optimization algorithm for path planning of UCAV. Appl Soft Comput 8:1712–1718

  • Coello CA (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inform Syst 1(3):269–308

  • Das PK, Pradhan SK, Patro SN, Balabantaray BK (2012) Artificial immune system based path planning of mobile robot. Stud Comput Intell 395:195–207

    Google Scholar 

  • Deepak BBVL, Parhi DR, Kundu S (2012) Innate immune based path planner of an autonomous mobile robot. Cent Eur J Comput Sci 2(2):2663–2671

  • Duan HB, Yu YX, Zhou R (2008) UCAV path planning based on ant colony optimization and satisficing decision algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation, pp 957–962, 2008

  • Duan HB, Yu YX, Zhang XY (2010) Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm. Simul Model Pract Theory 18(8):1104–1115

    Article  Google Scholar 

  • Foo JL, Knutzon J, Kalivarapu V, Oliver J, Winer E (2009) Path planning of unmanned aerial vehicles using B-splines and particle swarm optimization. J Aerosp Comput Inform Commun 6(4):271–290

    Article  Google Scholar 

  • Fu YG, Ding MY, Zhou CP (2012) Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans Syst Man Cybern Part A Syst Hum 42(2):511–526

    Article  Google Scholar 

  • Garcia MAP, Montiel O, Castillo O, Sepulveda R, Melin P (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 1(2):1102–1110

    Article  Google Scholar 

  • Gong DW, Zhang JH, Zhang Y (2011) Multi-objective particle swarm optimization for robot path planning in environment with danger sources. J Comput 6(8):1554–1561

    Article  Google Scholar 

  • Hao JJ, Kang ZL (2005) Plant physiology, Chaps. 7, 8. Chemical Industry Press, Beijing (in Chinese)

  • Hassanzadeh I, Madani K, Badamchizadeh MA (2010) Mobile robot path planning based on shuffled frog leaping optimization algorithm. In: Proceedings of 6th annual IEEE conference on automation science and engineering, pp 680–685, 2010

  • Hossain MA, Ferdous I (2015) Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. Robot Auton Syst 64:137–141

  • Jati A, Singh G, Rakshit P, Konar A, Kim E, Nagar AK (2012) A hybridisation of improved harmony search and bacterial foraging for multi-robot motion planning. In: Proceedings of WCCI 2012 IEEE world congress on computational intelligence, 2012

  • Karc A (2007) Natural inspired computational intelligence method: saplings growing up algorithm. In: Proc of IEEE Int Conf Computational Cybernetics, Gammarth, Tunisia

  • Li BL, Liu LJ, Zhang QH, Lv DJ, Zhang YF, Zhang JH, Shi XL (2014) Path planning based on firefly algorithm and Bezier curve. In: Proceeding of the IEEE international conference on information and automation, pp 630–633, 2014

  • Li B, Gong LG, Yang WL (2014) An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning. Sci World J 2014:1–10

    Google Scholar 

  • Liang XD, Li LY, Wu JG, Chen HN (2013) Mobile robot path planning based on adaptive bacterial foraging algorithm. J Cent South Univ 20:3391–3400

    Article  Google Scholar 

  • Li T, Su WL (2007) Research on plant growth simulation algorithm based on finite element method. In: Proceedings of second international conference on innovative computing, information and control, p 419, 2007

  • Li T, Su WL, Wang CF (2004) A global optimization bionics algorithm for solving integer programming - Plant growth simulation algorithm. In: Proceedings of international conference on management science and engineering, pp 531–535, 2004

  • Liu W, Niu B, Chen HN, Zhu YL (2013) Robot path planning using bacterial foraging algorithm. J Comput Theor Nanosci 10:2890–2896

    Article  Google Scholar 

  • Liu C, Zhao YX, Gao F, Liu LQ (2015) Three-dimensional path planning method for autonomous underwater vehicle based on modified firefly algorithm. Math Probl Eng 2015:1–10

    Google Scholar 

  • Liu C, Gao ZQ, Zhao WH (2012) A new path planning method based on firefly algorithm. In: Proceedings of 2012 fifth international joint conference on computational sciences and optimization, pp 775–778, 2012

  • Luh GC, Liu WW (2008) An immunological approach to mobile robot reactive navigation. Appl Soft Comput 8(1):30–45

    Article  Google Scholar 

  • Ma QZ, Lei XJ (2010) Application of artificial fish school algorithm in UCAV path planning. In: IEEE fifth international conference on bio-inspired computing: theories and applications, pp 555–559, 2010

  • Mernik M, Liu S-H, Karaboga MD, Crepinšek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inform Sci 291:115–127

    Article  MathSciNet  Google Scholar 

  • Miyaji T, Onishi I (2007) Mathematical analysis to an adaptive network of the Plasmodium system. Hokkaido Math J 36(2):445–465

  • Mohanty PK, Parhi DR (2014) A new real time path planning for mobile robot navigation using invasive weed optimization algorithm. In: Proceedings of ASME 2014 gas turbine india conference, p V001T07A002, 2014

  • Mohanty PK, Kumar S, Parhi DR (2014) A new ecologically inspired algorithm for mobile robot navigation. Adv Intell Syst Comput 327:755–762

    Article  Google Scholar 

  • Mohanty PK, Parhi DR (2014) A new efficient optimal path planner for mobile robot based on invasive weed optimization algorithm. Front Mech Eng 9(4):317–330

    Article  Google Scholar 

  • Mo HW, Meng LL (2012) Robot path planning based on differential evolution in static environment. Int J Digital Content Technol Appl 6(20):122–129

    Article  Google Scholar 

  • Ni JJ, Yin XH, Chen JF, Li XY (2014) An improved shuffled frog leaping algorithm for robot path planning. In: Proceedings of 2014 10th international conference on natural computation, pp 545–549, 2014

  • Peng JS, Li X, Qin ZQ, Luo G (2013) Robot global path planning based on improved artificial fish-swarm algorithm. Res J Appl Sci Eng Technol 5(6):2042–2047

    Google Scholar 

  • Rao RS, Narasimham SVL (2008) Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm. Int J Electr Power Energy Energy Syst Eng 1(2):123–130

    Google Scholar 

  • Salhi A, Fraga ES (2011) Nature-inspired optimization approaches and the new plant propagation algorithm. In: Proceedings of the the international conference on numerical analysis and optimization, Yogyakarta, Indonesia, pp K2-1–K2-8, 2011

  • Sulaiman M, Salhi A, Selamoglu BI, Kirikchi OB (2014) A plant propagation algorithm for constrained engineering optimization problems. Math Probl Eng 2014:1–10

  • Tan GZ, He H, Sloman A (2007) Ant colony system algorithm for real-time globally optimal path planning of mobile robots. Acta Automatica Sinica 33(3):279–285

    Article  MATH  Google Scholar 

  • Wang YN, Lee TS, Tsao TF (2007) Plan on obstacle-avoiding path for mobile robots based on artificial immune algorithm. Lect Notes Comput Sci 4491:694–703

    Article  Google Scholar 

  • Wang GG, Guo LH, Duan H, Liu L, Wang HQ (2012) A modified firefly algorithm for UCAV path planning. Int J Hybrid Inform Technol 5(3):123–144

    Google Scholar 

  • Xu CF, Duan HB, Liu F (2010) Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning. Aerosp Sci Technol 14(8):535–541

    Article  Google Scholar 

  • Zhang XG, Zhang YJ, Zhang ZL, Mahadevan S (2014) Rapid Physarum algorithm for shortest path problem. Appl Soft Comput 23:19–26

    Article  Google Scholar 

  • Zhang ZR, Yin JY (2012) The study on mobile robot path planning based on frog leaping algorithm. Adv Mater Res 3:490–495

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the support of Aerospace Science and Industry Fund.

Author information

Authors and Affiliations

  1. School of Aeronautic Science and Engineering, Beihang University, Beijing, 100191, China

    Yaoming Zhou, Yongchao Wang & Xuzhi Chen

  2. Hang Zhou Sysmagic Technology CO., LTD, Hangzhou, 311121, China

    Lei Zhang

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Kan Wu

Authors
  1. Yaoming Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongchao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuzhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kan Wu.

Ethics declarations

Conflict of interest

The authors have no conflict of interests.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Wang, Y., Chen, X. et al. A novel path planning algorithm based on plant growth mechanism. Soft Comput 21, 435–445 (2017). https://doi.org/10.1007/s00500-016-2045-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2045-x

Keywords

Search

Navigation