Skip to main content
SpringerLink
Log in

Heterogeneous-ants-based path planner for global path planning of mobile robot applications

  • Regular Papers
  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

Mobile robots can be applied to a wide range of problems, and the demand for these applications has risen in recent years, increasing interest in the study of mobile robotics. Many studies have examined the path planning problem, one of the most important issues in mobile robotics. However, the grid paths found by traditional planners are often not the true shortest paths or are not smooth because their potential headings are artificially constrained to multiples of 45 degrees. These paths are unfit for application to mobile robots because the high number of heading changes increases the energy required to move the mobile robot. Some studies have proposed a post-processing step to smooth the grid path. However, in this case, the post-smoothed path may not necessarily find the true shortest path because the post-smoothed path is still constrained to headings of multiples of 45 degrees. This study attempts to develop a global path planner that can directly find an optimal and smoother path without post-processing to smooth the path. We propose a heterogeneous-ants-based path planner (HAB-PP) as a global path planner to overcome the shortcomings mentioned above. The HAB-PP was created by modifying and optimizing the global path planning procedure from the ant colony optimization (ACO) algorithm. The proposed algorithm differs from the traditional ACO path planning algorithm in three respects: modified transition probability function for moving ants, modified pheromone update rule, and heterogeneous ants. The simulation results demonstrate the effectiveness of the HAB-PP.

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

Similar content being viewed by others

References

  1. G. Chesi and Y. S. Hung, “Global Path-planning for constrained and optimal visual servoing,” IEEE Trans. on Robotics, vol. 23, no. 5, pp. 1050–1060, October 2007. [click]

    Article  Google Scholar 

  2. J.-W. Lee, D.-H. Lee, and J.-J. Lee, “Global path planning using improved ant colony optimization algorithm through bilateral cooperative exploration,” Proc. of the 5th IEEE Int. Conf. on Digital Ecosystems and Technologies, Daejeon, pp. 109–113, May-June 2011.

    Google Scholar 

  3. D. R. Parhi and J. C. Mohanta, “Navigational control of several mobile robotic agents using Petri-potential-fuzzy hybrid controller,” Applied Soft Computing, vol. 11, no. 4, pp. 3546–3557, June 2011. [click]

    Article  Google Scholar 

  4. M. Davoodi, M. Abedin, B. Banyassady, and P. Khanteimouri, “An optimal algorithm for two robots path planning problem on the grid,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1406–1414, December 2013. [click]

    Article  Google Scholar 

  5. S. Panov and S. Koceski, “Metaheuristic global path planning algorithm for mobile robots,” Int. J. of Reasoningbased Intelligent Systems, vol. 7, no. 1–2, pp. 35–41, 2015. [click]

    Article  Google Scholar 

  6. S.-H. Park and B.-H. Lee, “A new analytical representation to robot path generation with collision avoidance through the use of the collision map,” Int. J. of Control, Automation, and Systems, vol. 4, no. 1, pp. 77–86, February 2006.

    Google Scholar 

  7. D. H. Kim, “Escaping route method for a trap situation in local path planning,”, Int. J. of Control, Automation, and Systems, vol. 7, no. 3, pp. 495–500, June 2009.

    Article  Google Scholar 

  8. B.-S. Choi, J.-W. Lee, J.-J. Lee, and K.-T. Park, “A hierarchical algorithm for indoor mobile robot localization using RFID sensor fusion,” IEEE Trans. on Industrial Electronics, vol. 58, no. 6, pp. 2226–2235, June 2011. [click]

    Article  Google Scholar 

  9. Q. Zhu, J. Hu, W. Cai, and L. Henschen, “A new robot navigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm,” Applied Soft Computing, vol. 11, no. 8, pp. 4667–4676, December 2011.

    Article  Google Scholar 

  10. X. Gao, Y. Ou, Y. Fu, Z. Li, D. Dai, L, Cui, Y. Zhou, and W. Guo, “A novel local path planning method considering both robot posture and path smoothness,” Proc. of the IEEE Int. Conf. on Robotics and Biomimetics, Shenzhen, pp. 172–178, December 2013.

    Google Scholar 

  11. U. A. Syed, F. Kunwar, and M. Iqbal, “Guided autowave pulse coupled neural network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots,” Robotics and Autonomous Systems, vol. 62, no. 4, pp. 474–486, April 2014. [click]

    Article  Google Scholar 

  12. S. Bandi and D. Thalmann, “Space discretization for efficient human navigation,” Computer Graphic Forums, vol. 17, no. 3, pp. 195–206, August 1998.

    Article  Google Scholar 

  13. P. Melchior, B. Orsoni, O. Lavialle, A. Poty, and A. Oustaloup, “Consideration of obstacle danger level in path planning using A* and Fast-Marching optimisation: comparative study,” Signal Processing, vol. 83, no. 11, pp. 2387–2396, November 2003. [click]

    Article  MATH  Google Scholar 

  14. J. Yao, C. Lin, X. Xie, A. j. Wang, and C.-C. Hung, “Path planning for virtual human motion using improved A* star algorithm,” Proc. of Seventh Int. Conf. on Information Technology: New Generations, Las Vegas, pp. 1154–1158, April 2010. [click]

    Google Scholar 

  15. J.-H. Seok, J.-W. Lee, J.-H. Wang, J.-J. Lee, and H. J. Lee, “A temporal path planner for solving information inconsistency in an integrated path planner,” Int. J. of Control, Automation, and Systems, vol. 11, no. 6, pp. 1232–1240, December 2013. [click]

    Article  Google Scholar 

  16. T. W. Manikas, K. Ashenayi, and R. L. Wainwright, “Genetic Algorithms for Autonomous Robot Navigation,” IEEE Instrumentation and Measurement Magazine, vol. 10, no. 6, pp. 26–31, December 2007. [click]

    Article  Google Scholar 

  17. J. Zhao, L. Zhu, G. Liu, G. Liu, and Z. Han, “A modified genetic algorithm for global path planning of searching robot in mine disasters,” Proc. of IEEE Int. Conf. on Mechatronics and Automation, Changchun, pp. 4936–4940, August 2009.

    Google Scholar 

  18. J. Berger, K. Jabeur, A. Boukhtouta, A. Guitouni, and A. Ghanmi, “A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment,” IEEE Congr. on Evolutionary Computation, Barcelona, pp. 1–8, July 2010.

    Google Scholar 

  19. S. C. Yun, V. Ganapathy, and L. O. Chong, “Improved Genetic Algorithms based Optimum Path planning for Mobile Robot,” Proc. of IEEE 11th Int. Conf. on Control Automation Robotics and Vision, Singapore, pp. 1565–1570, December 2010.

    Google Scholar 

  20. D. Zhao and J. Yi, “Robot planning with artificial potential field guided ant colony optimization algorithm,” Int. Conf. of Natural Computation, Xian, pp. 222–231, September 2006. [click]

    Chapter  Google Scholar 

  21. M. A. Porta Garcia, O. Montiel, O. Castillo, R. Sepulveda, and P. Melin, “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Applied Soft Computing, vol. 9, no. 3, pp. 1102–1110, June 2009. [click]

    Article  Google Scholar 

  22. K. Ioannidis, G. Ch. Sirakoulis, and I. Andreadis, “Cellular ants: A method to create collision free trajectories for a cooperative robot team,” Robotics and Autonomous Systems, vol. 59, no. 2, pp. 113–127, February 2011. [click]

    Article  Google Scholar 

  23. H. Mo and L. Xu, “Research of biogeography particle swarm optimization for robot path planning,” Neurocomputing, vol. 148, pp. 91–99, January 2015. [click]

    Article  Google Scholar 

  24. B. Tang, Z. Zhu, and J. Luo, “Hybridizing particle swarm optimization and differential evolution for the mobile robot global path planning,” Int. J. of Advanced Robotic Systems, vol. 13, pp. 1–17, May 2016.

    Article  Google Scholar 

  25. H. Duan, Y. Yu, X. Zhang, and S. Shao, “Three-dimension path planning for UCAV using hybrid meta-heuristic ACODE algorithm,” Simulation Modeling Practice and Theory, vol. 18, no. 8, pp. 1104–1115, September 2010.

    Article  Google Scholar 

  26. M. A. Contreras-Cruz, V. Ayala-Ramirez, and U. H. Hernandez-Belmonte, “Mobile robot path planning using artificial bee colony and evolutionary programming,” Applied Soft Computing, vol. 30, pp. 319–328, May 2015. [click]

    Article  Google Scholar 

  27. Y. Lan, J. Huang, and X. Chen, “Research on intelligent vehicle path planning based on artificial immune network algorithm,” J. of Information & Computational Science, vol. 12, no. 16, pp. 6023–6032, November 2015.

    Article  Google Scholar 

  28. J.-W. Lee, Y.-I. Cho, M. Sugisaka, and J.-J. Lee, “Study of novel heterogeneous ant colony optimization algorithm for global path planning,” IEEE Int. Symp. on Industrial Electronics, Bari, pp. 1961–1966, July 2010.

    Google Scholar 

  29. J.-W. Lee, B.-S. Choi, K.-T. Park, and J.-J. Lee, “Comparison between heterogeneous ant colony optimization algorithm and genetic algorithm for global path planning of mobile robot,” IEEE Int. Symp. on Industrial Electronics, Gdansk, pp. 881–886, June 2011. [click]

    Google Scholar 

  30. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. on Systems, Man, and Cybernetics - Part B, vol. 26, no. 1, pp. 29–41, February 1996. [click]

    Article  Google Scholar 

  31. M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, April 1997. [click]

    Article  Google Scholar 

  32. L. M. Gambardella, E. D. Taillard, and M. Dorigo, “Ant colonies for the quadratic assignment problem,” J. of the Operational Research Society, vol. 50, no. 2, pp. 167–176, February 1999.

    Article  MATH  Google Scholar 

  33. J.-W. Lee, B.-S. Choi, and J.-J. Lee, “Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones,” IEEE Trans. on Industrial Informatics, vol. 7, no. 3, pp. 419–427, August 2011. [click]

    Article  Google Scholar 

  34. J.-W. Lee, J.-J. Kim, B.-S. Choi, and J.-J. Lee, “Improved ant colony optimization algorithm by potential field concept for optimal path planning,” Proc. of 8th IEEE-RAS Int. Conf. on Humanoid Robots, Daejeon, pp. 662–667, December 2008.

    Google Scholar 

  35. R. Siegwart and Illah R. Nourbakhsh, Introduction on Autonomous Mobile Robots, MIT Press, 2004.

    Google Scholar 

  36. J.-W. Lee and J.-J. Lee, “Novel ant colony optimization algorithm with path crossover and heterogeneous ants for path planning,” IEEE Int. Conf. on Industrial Technology, Viña del Mar, pp. 559–564, March 2010. [click]

    Google Scholar 

  37. J.-W. Lee, J.-J. Kim, and J.-J. Lee, “Improved ant colony optimization algorithm by path crossover for optimal path planning,” IEEE Int. Symp. on Industrial Electronics, Seoul, pp. 1996–2000, July 2009. [click]

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Kyungpook National University (KNU), 80 Daehakro, Bukgu, Daegu, 41566, Korea

    Joonwoo Lee

Authors
  1. Joonwoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joonwoo Lee.

Additional information

Recommended by Associate Editor Kang-Hyun Jo under the direction of Editor Fuchun Sun.

Joonwoo Lee received his B.S. degree in electronics and electrical engineering from Pusan National University, Busan, Korea, in 2007, and an M.S. degree in robotics from Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 2009, where also received his Ph.D. degree in electrical engineering, in 2014. From 2014 to 2015, he worked as a postdoctoral researcher in the department of robotics and mechatronics, Advanced Manufacturing Systems Research Division, Korea Institute of Machinery and Materials (KIMM). He is currently working as an assistant professor in the department of electrical engineering, Kyungpook National University (KNU) since Sep. 2015. His research interests include swarm intelligence, swarm robotics, metaheuristics, intelligence control, and smart machine.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, J. Heterogeneous-ants-based path planner for global path planning of mobile robot applications. Int. J. Control Autom. Syst. 15, 1754–1769 (2017). https://doi.org/10.1007/s12555-016-0443-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-016-0443-6

Keywords

Search

Navigation