Advertisement

Journal of Mechanical Science and Technology

, Volume 32, Issue 12, pp 5563–5571 | Cite as

Modified A-star algorithm for modular plant land transportation

  • Nam Kyu Kang
  • Ho Joon Son
  • Soo-Hong LeeEmail author
Article
  • 40 Downloads

Abstract

Many common path optimization algorithms are available. However, problems arise when a general route optimization algorithm is applied to land transportation of large cargo, such as a modular plant. The large and heavy structure of a modular plant can lead to a loss of time depending on the curve of the road. This problem is more critical when traveling through large turns, which may also cause mechanical problems. Therefore, curves are essential parameters for modular plant land transportation. In this research, we show the importance of angles in the path via multi-body dynamic simulations and finite element analysis. Based on these results, we constructed a pathfinding algorithm that considers the importance of angles. The traditional A-star algorithm considers only distance as a cost, whereas our modified A-star algorithm considers both distance and angle as costs. Our goal is to improve traditional A-star algorithms and optimize them for modular plant land transportation.

Keywords

A-star algorithm Multi-body dynamics simulation Plant land transportation Optimized path finding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    I. Flinsenberg, Route planning algorithms for car navigation (2004).Google Scholar
  2. [2]
    C. Bodenhorn et al., Personalizing onboard route re–planning for recon, attack, and special operations missions, AHS Avionics and Crew Systems Technical Specialists' Meeting, Philadelphia, PA. (1997).Google Scholar
  3. [3]
    J.–C. Latombe, Robot motion planning, Springer Science & Business Media, 124 (2012).Google Scholar
  4. [4]
    A. M. Dalavi, J. P. Padmakar and T. P. Singh, Tool path planning of hole–making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm, J. of Computational Design and Engineering, 3 (3) (2016) 266–273.CrossRefGoogle Scholar
  5. [5]
    Q. Zhang and M.–Y. Zhao, Minimum time path planning of robotic manipulator in drilling/spot welding tasks, J. of Computational Design and Engineering, 3 (2) (2016) 132–139.CrossRefGoogle Scholar
  6. [6]
    S. He et al., A chord error conforming tool path B–spline fitting method for NC machining based on energy minimization and LSPIA, J. of Computational Design and Engineering, 2 (4) (2015) 218–232.MathSciNetCrossRefGoogle Scholar
  7. [7]
    R. Geisberger, Advanced route planning in transportation networks, Diss. Karlsruher Instituts fr Technologie (2011).Google Scholar
  8. [8]
    I. A. Hameed, D. D. Bochtis and C. G. Sorensen, Driving angle and track sequence optimization for operational path planning using genetic algorithms, Applied Engineering in Agriculture, 27 (6) (2011) 1077–1086.CrossRefGoogle Scholar
  9. [9]
    C.–B. Noh, M.–H. Kim and M.–C. Lee, Path planning for the shortest driving time considering UGV driving characteristic and driving time and its driving algorithm, J. of Korea Robotics Society, 8 (1) (2013) 43–50.CrossRefGoogle Scholar
  10. [10]
    R. J. Szczerba et al., Robust algorithm for real–time route planning, IEEE Transactions on Aerospace and Electronic Systems, 36 (3) (2000) 869–878.CrossRefGoogle Scholar
  11. [11]
    L. García, F. Wilson and J. Innes, Heavy truck dynamic rollover: Effect of load distribution, cargo type, and road design characteristics, Transportation Research Record: J. of the Transportation Research Board, 1851 (2003) 25–31.CrossRefGoogle Scholar
  12. [12]
    J. Yao et al., Path planning for virtual human motion using improved A* star algorithm, 2010 Seventh International Conference on Information Technology: New Generations (ITNG), IEEE (2010).Google Scholar
  13. [13]
    H. Salehinejad et al., Combined A*–ants algorithm: a new multi–parameter vehicle navigation scheme, arXiv preprint arXiv:1504.07329 (2015).Google Scholar
  14. [14]
    F. Duchoň et al., Path planning with modified A–star algorithm for a mobile robot, Procedia Engineering, 96 (2014) 59–69.CrossRefGoogle Scholar
  15. [15]
    H.–T. Hwang et al., Development of a transportability evaluation system using swept path analysis and multi–body dynamic simulation, J. of Mechanical Science and Technology, 31 (11) (2017) 5359–5365.CrossRefGoogle Scholar
  16. [16]
    E. Rohmer, S. P. N. Singh and M. Freese, V–REP: A versatile and scalable robot simulation framework, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2013).Google Scholar
  17. [17]
    L. L. Ojeda, A. Chasse and R. Goussault, Fuel consumption prediction for heavy–duty vehicles using digital maps, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE (2017).CrossRefGoogle Scholar
  18. [18]
    W. Jigang et al., Algorithm for time–dependent shortest safe path on transportation networks, Procedia Computer Science, 4 (2011) 958–966.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringYonsei UniversitySeoulKorea

Personalised recommendations