Soft Computing

, Volume 21, Issue 19, pp 5829–5839 | Cite as

An improved ant colony algorithm for robot path planning

  • Jianhua Liu
  • Jianguo Yang
  • Huaping Liu
  • Xingjun Tian
  • Meng Gao
Methodologies and Application

Abstract

To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.

Keywords

Mobile robot Ant colony algorithm Pheromone diffusion Local path optimization 

References

  1. Borenstein J, Koren Y (1991) Histogramic in motion mapping for mobile robot obstacle avoidance. IEEE J Robotics Autom 7(4):535–539CrossRefGoogle Scholar
  2. Botzheim J, Toda Y, Kubota N (2012) Bacterial memetic algorithm for offline path planning of mobile robots. Memet Comput 4(1):73–86CrossRefGoogle Scholar
  3. Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robotics Autom 2(1):14–23CrossRefGoogle Scholar
  4. Castillo O, Trujillo L, Melin P (2007) Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots. Soft Comput 11(3):269–279CrossRefGoogle Scholar
  5. Cheng CT, Fallahi K, Leung H, Tse CK (2010) An auvs path planner using genetic algorithms with a deterministic crossover operator. In: International conference on robotics and automation (ICRA), pp 2995–3000Google Scholar
  6. Erin B, Abiyev R, Ibrahim D (2010) Teaching robot navigation in the presence of obstacles using a computer simulation program. Proc Soc Behav Sci 2(2):565–571CrossRefGoogle Scholar
  7. Geng P, Wang Z, Zhang Z, Xiao Z (2012) Image fusion by pulse couple neural network with shearlet. Opt Eng 51(6):067,005–067,005–7Google Scholar
  8. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  9. Kang B, Wang X, Liu F (2014) Path planning of searching robot based on improved and ant colony algorithm. J Jilin Univ Eng Technol Ed 44(04):1062–1068Google Scholar
  10. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robotics Res 5(1):90–98CrossRefGoogle Scholar
  11. Lim KK, Ong YS, Lim MH, Chen X, Agarwal A (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994CrossRefGoogle Scholar
  12. Liu Z, Zhang Y, Jing Z, Wu J (2010) Using combination of ant algorithm an immune algorithm to solve tsp. Control Decis 25(5):695–700MathSciNetMATHGoogle Scholar
  13. Luo D, Wu S (2010) Ant colony optimization with potential field heuristic for robot path planning. Syst Eng Electron 32(6):1277–1280MathSciNetGoogle Scholar
  14. Ma T, Zhou J, Tang M, Tian Y, Al-Dhelaan A, Al-Rodhaan M, Lee S (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910CrossRefGoogle Scholar
  15. Mavrovouniotis M, Yang S (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15(7):1405–1425CrossRefGoogle Scholar
  16. Parpinelli RS, Lopes HS (2015) A computational ecosystem for optimization: review and perspectives for future research. Memet Comput 7(1):29–41CrossRefGoogle Scholar
  17. Peng G, Wang Z, Liu S, Zhuang S (2015) Image fusion by combining multiwavelet with nonsubsampled direction filter bank. Soft Comput 1–13. doi:10.1007/s00500-015-1893-0
  18. Savsani P, Jhala RL, Savsani V (2014) Effect of hybridizing biogeography-based optimization (bbo) technique with artificial immune algorithm (aia) and ant colony optimization (aco). Appl Soft Comput 21(5):542–553CrossRefGoogle Scholar
  19. Shi E, Chen M, Li J, Huang Y (2014) Research on method of global path-planning for mobile robot based on ant-colony algorithm. Trans Chin Soc Agric Mach 45(6):53–57Google Scholar
  20. Shuang B, Chen J, Li Z (2011) Study on hybrid ps-aco algorithm. Appl Intel 34(1):64–73CrossRefGoogle Scholar
  21. Sttzle T, Hoos H (1997) Improvement on the ant system: introducing max-min ant system. Proceed. International conference on artificial neural networks and genetic algoritms. Springer-Verlag, Vienna, pp 246–250Google Scholar
  22. Wang P, Feng Z, Huang X (2008) An improved ant algorithm for mobile robot path planning. Robot 30(6):554–560Google Scholar
  23. Wang Y (2015) Hybrid maxmin ant system with four vertices and three lines inequality for traveling salesman problem. Soft Comput 19(10):585–596CrossRefGoogle Scholar
  24. Wei J, Cheng F, Zhao D, Tao Y, Ding S, Lü J (2013) Obstacle avoidance method of apple harvesting robot manipulator. Trans Chin Soc Agric Mach 44(11):254–259Google Scholar
  25. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406CrossRefGoogle Scholar
  26. Wu X, Guo B, Wang J (2009) Mobile robot path planning algorithm based on particle swarm optimization of cubic splines. Robot 31(6):556–560Google Scholar
  27. Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246CrossRefGoogle Scholar
  28. Xu X, Zhu Q (2012) Multi-artificial fish-swarm algorithm and a rule library based dynamic collision avoidance algorithm for robot path planning in a dynamic environment. Acta Electron Sin 40(8):1694–1700Google Scholar
  29. Zhao Y, Zhang H, Kong G (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intel Fuzzy Syst 28(2):961–973Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jianhua Liu
    • 1
    • 2
  • Jianguo Yang
    • 1
  • Huaping Liu
    • 3
  • Xingjun Tian
    • 2
  • Meng Gao
    • 2
  1. 1.College of Mechanical EngineeringDonghua UniversityShanghaiChina
  2. 2.College of Electrical and Electronic EngineeringShijiazhuang Tiedao UniversityShijiazhuangChina
  3. 3.Key Laboratory of Intelligent Technology and SystemsTsinghua UniversityBeijingChina

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