Modified crash-minimization path designing approach for autonomous material handling robot

  • Suvranshu Pattanayak
  • Bibhuti Bhusan ChoudhuryEmail author
Special Issue


The potentiality of particle swarm optimization (PSO), artificial potential field (APF) and improved PSO (IPSO) approaches are exploited in this paper for designing and generating the best possible optimum trajectory for a mobile material handling robot. The paper has an aim to develop a robot, which used to create a bridge between primary and secondary handling system. This principally saves the transportation time, so the total cycle time and manufacturing lead time can be reduced. For performing simulation practice two practical surroundings has been developed with a layout of institute machine shop and advanced laboratory. Objective of this study are to slash the track size, computational time, degree of crash risk, travel time and better path smoothness. To check the robustness and applicability of the approaches a comparison report has been prepared among their simulation and experimental outcomes. For surrounding setup-I; PSO algorithm provides 35.3568 m track length, 20.778 s computational time and 280.098 s travel time. Similarly the trajectory dimension, computational time and travel time generated in APF approach is 44.1632 m, 10.923 s and 343.441 s respectively. 33.6278 m track size, 20.651 s computational time and 266.612 s travel time is developed by IPSO approach. For surrounding setup-II; PSO algorithm provides 14.7769 m track length, 18.655 s computational time and 117.063 s travel time. Similarly the trajectory dimension, computational time and travel time generated in APF approach is 22.8645 m, 27.623 s and 189.077 s respectively. 13.0859 m track size, 18.507 s computational time and 103.769 s travel time is developed by IPSO approach. From comparison study it is found that, IPSO approach delivers smoother less collision risk associated path having less length, and computational time irrespective to environment complexity. The approaches are relying on computer programming commands, which are written, compiled and run using MATLAB software.


Material handling robot Track designing PSO APF IPSO Crash avoidance 



  1. 1.
    Chiddarwar SS, Babu NR (2011) Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence. Robot Auton Syst 59:508–518CrossRefGoogle Scholar
  2. 2.
    Pal A, Tiwari R, Shukla A (2012) Modified A* algorithm for mobile robot path planning. In: Patnaik S, Yang Y-M (eds) Soft computing techniques in vision science, vol 395. Springer, Berlin, pp 183–193CrossRefGoogle Scholar
  3. 3.
    Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. Expert Syst Appl 39:3817–3831CrossRefGoogle Scholar
  4. 4.
    Li P, Duan HB (2012) Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci China Technol Sci 55(10):2712–2719CrossRefGoogle Scholar
  5. 5.
    Chen X, Kong Y, Fang X, Wu Q (2013) A fast two-stage ACO algorithm for robotic path planning. Neural Comput Appl 22:313–319CrossRefGoogle Scholar
  6. 6.
    Qu H, Xing K, Alexander T (2013) An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120:509–517CrossRefGoogle Scholar
  7. 7.
    Davoodi M, Abedin M, Banyassady B, Khanteimouri P, Mohades A (2013) An optimal algorithm for two robots path planning problem on the grid. Robot Auton Syst 61(12):1406–1414CrossRefGoogle Scholar
  8. 8.
    Zielinska T, Kasprzak W, Szynkiewicz W, Zieliński C (2014) Path planning for robotized mobile supports. Path planning for robotized mobile supports. Mech Mach Theory 78:51–64CrossRefGoogle Scholar
  9. 9.
    Lin Q, Li J, Du Z, Chen J, Ming Z (2015) A novel multi-objective particle swarm optimization with multiple search strategies. Eur J Oper Res 247(3):732–744MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Mo H, Xu L (2015) Research of biogeography particle swarm optimization for robot path planning. Neurocomputing 148:91–99CrossRefGoogle Scholar
  11. 11.
    Montiel O, Rosas UO, Sepúlveda R (2015) Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 42(12):5177–5191CrossRefGoogle Scholar
  12. 12.
    Das PK, Behera HS, Jena PK, Panigrahi BK (2016) Multi-robot path planning in a dynamic environment using improved gravitational search algorithm. J Electr Syst Inf Technol 3(2):295–313Google Scholar
  13. 13.
    Das PK, Behera HS, Panigrahi BK (2016) A hybridization of an improved particleswarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evolut Comput 28:14–28CrossRefGoogle Scholar
  14. 14.
    Das PK, Behera HS, Panigrahi BK (2016) Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity. Eng Sci Technol Int J 19(1):651–669CrossRefGoogle Scholar
  15. 15.
    Kovacs B, Szayer G, Tajti F, Burdelis M, Korondi P (2016) A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robot Auton Syst 82:24–34CrossRefGoogle Scholar
  16. 16.
    Mac TT, Copot C, Tran DT, Keyser RD (2017) A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl Soft Comput 59:68–76CrossRefGoogle Scholar
  17. 17.
    Panda MR, Das P, Pradhan S (2017) Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment. J King Saud Univ Comput Inf Sci. Google Scholar
  18. 18.
    Li G, Chou W (2018) Path planning for mobile robot using self-adaptive learning particle swarm optimization. Sci China Inf Sci 61:052204:1–052204:18MathSciNetGoogle Scholar
  19. 19.
    Wang B, Li S, Guo J, Chen Q (2018) Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm. Neurocomputing 282:42–51CrossRefGoogle Scholar
  20. 20.
    Bayat F, Nia SN, Aliyari M (2018) Mobile robots path planning: electrostatic potential field approach. Expert Syst Appl 100:68–78CrossRefGoogle Scholar
  21. 21.
    Zhang X, Liu J (2018) Effective motion planning strategy for space robot capturing targets under consideration of the berth position. Acta Astronaut 148:403–416CrossRefGoogle Scholar
  22. 22.
    Zhou Z, Wang J, Zhu Z, Yang D, Wu J (2018) Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik 158:639–651CrossRefGoogle Scholar
  23. 23.
    Pattanayak S, Sahoo SC, Choudhury BB (2018) An effective path planning of a mobilerobot. Soft Comput Data Anal Adv Intell Syst Comput 758:175–182. Google Scholar
  24. 24.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol IV, pp 1942–1948. IEEE service center, Piscataway, NJGoogle Scholar
  25. 25.
    Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5(1):90–98CrossRefGoogle Scholar
  26. 26.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation, pp 69–73. IEEE Press, Piscataway, NJGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Indira Gandhi Institute of TechnologyDhenkanalIndia

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