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Robot Path Planning Based on Dijkstra’s Algorithm and Genetic Algorithm

  • Yang Liu
  • Junhua Liang
  • Jing Li
  • Zhisheng Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

In this paper, a robot path planning method based on Dijkstra’s Algorithm and Genetic Algorithms is proposed. This method works for any appointed start point and end point in an arbitrary rectangular workspace with arbitrary rectangular obstacles whose edges are perpendicular or parallel to the walls of the workspace. Firstly, use Dijkstra’s Algorithm to find a path among all the midpoints of obstacles’ vertices and the walls of the workspace. Then use Genetic Algorithm to optimize the path gotten from Dijkstra’s Algorithm and finally generate an optimal path for the robot from the start point to the end point.

Keywords

Robot path planning Dijkstra’s algorithm Genetic algorithm 

Notes

Acknowledgements

This work was supported by Major Scientific Research Project in Higher School in Hebei Province (Grant No. ZD20131085), Funding Project of Science & Technology Research and Development in Hebei North University (Grant No. ZD201301), Major Projects in Hebei Food and Drug Administration (Grant No. ZD2015017), Major Funding Project in Hebei Health Department (Grant No. ZL20140127), Youth Funding Science and Technology Projects in Hebei Higher School (Grant No. QN2016192), with Hebei Province Population Health Information Engineering Technology Research Center.

Bibliography

  1. 1.
    Shortest Path Algorithms, MIT’s Introduction to AlgorithmsGoogle Scholar
  2. 2.
    Basic Shortest path Algorithms, Andrew V. GoldbergGoogle Scholar
  3. 3.
    An Overview of Evolutionary Computation, Xin Yao, Chinese Journal of Advanced Software ResearchGoogle Scholar
  4. 4.
    R. Anthony and E. D. Reilly (1993), Encyclopedia of Computer Science, Chapman & Hall. U. Aybars, K. Serdar, C. Ali, Muhammed C. and Ali A (2009), Genetic Algorithm based solution of TSP on a sphere, Mathematical and Computational Applications, Vol. 14, No. 3, pp. 219–228.Google Scholar
  5. 5.
    B. Korte (1988), Applications of combinatorial optimization, talk at the 13th International Mathematical Programming Symposium, Tokyo.Google Scholar
  6. 6.
    E. L. Lawler, J. K. Lenstra, A. H. G. RinnooyKan, and D. B. Shmoys (1985), The Traveling Salesman Problem, John Wiley & Sons, Chichester.Google Scholar
  7. 7.
    H. Holland (1992), Adaptation in natural and artificial systems, Cambridge, MA, USA: MIT Press.Google Scholar
  8. 8.
    H. Nazif and L.S. Lee (2010), Optimized CrosSover Genetic Algorithm for Vehicle Routing Problem with Time Windows, American Journal of Applied Sciences 7 (1): pg. 95–101.Google Scholar
  9. 9.
    R. Braune, S. Wagner and M. Affenzeller (2005), Applying Genetic Algorithms to the Optimization of Production Planning in a real world Manufacturing Environment, Institute of Systems Theory and Simulation Johannes Kepler University.Google Scholar
  10. 10.
    Z. Ismail, W. Rohaizad and W. Ibrahim (2008), Travelling Salesman problem for solving Petrol Distribution using Simulated Annealing, American Journal of Applied Sciences 5(11): 1543–1546.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yang Liu
    • 1
  • Junhua Liang
    • 1
  • Jing Li
    • 1
  • Zhisheng Zhao
    • 1
  1. 1.School of Information Science and EngineeringHebei North UniversityZhangjiakouChina

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