A New Artificial Immune Network Model for Mobile Robot Path Planning

Abstract:

To solve the mobile robot path planning in uncertain environments, a new path planning algorithm is presented on the basis of the biological immune network. The environment surrounding the robot is taken as the antigen, and the behavior strategy of robot is taken as the antibody. The selection model of antibody concentration is defined based on the Jernes idiotypic immune network hypothesis, and the mobile robot path planning is realized through the selection of the antibody concentration. The simulation of path planning for mobile robot in multi-obstacle environments shows that the robot can find a safe path in complicated environments, which verifies the better adaptivity of proposed planning model. The simulation in dynamic environments shows that the robot can safely avoid all dynamic obstacles, which verifies the better flexility of new algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 416-417)
• Pages: 757-761
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.416-417.757
• Online since: September 2013
• Authors: Ming Xin Yuan, Pan Pan Zhang, Han Yang Li, Shuai Cheng, Yi Shen
• Keywords: Immune Network, Path Planning, Robot, Uncertain Environment

References

[1] Yuan M.X., Wang S. A, Wu C. Y (2010) A Novel Immune Network Strategy for Robot Path Planning in Complicated Environments. Journal of Intelligent and Robotic Systems 60(1): 111-131.

DOI: 10.1007/s10846-010-9408-9

[2] Liu, J., Yang, D. Y (2007) Path planning based on double-layer genetic algorithm. In: Proceeding of the Third International Conference on Natural Computation 13(4): 357-361.

DOI: 10.1109/icnc.2007.546

[3] Liu, G.Q., Li, T.J., Li, Y. P (2005) The ant algorithm for solving robot path planning problem. In: Proceeding of the Third International Conference on Information Technology and Applications 12(3): 25-27.

DOI: 10.1109/icita.2005.268

[4] Ge, S.S., Cui, Y. J (2002) Dynamic motion planning for mobile robots using potential field method. Auton. Robots 13(3): 207-222.

[5] Saboori I., Menhaj M.B., Karimi B (2006) Optimal Robot Path Planning Based on Fuzzy Model of Obstacles. In: Proceedings of the 2006 IEEE International Conference on Industrial Electronics, Paris, France 35(3): 383-387.

DOI: 10.1109/iecon.2006.347283

[6] Ghatee M., Mohades A (2009) Motion planning in order to optimize the length and clearance applying a Hopfield neural network. Expert Syst. Applications 36(3): 4688-4695.

DOI: 10.1016/j.eswa.2008.06.040

[7] Yuan M.X., Wang S.A., Wu C. Y (2010) Hybrid Ant Colony and Immune Network Algorithm Based on Improved APF for Optimal Motion Planning. ROBOTICA 28(6): 833-846.

DOI: 10.1017/s0263574709990567

[8] Ishiguro A., Watanabe Y., Kondo T (1999).

[9] Ishiguro A., Watanabe Y., Kondo T (1997) A Robot with a Decentralized Consensus-making Mechanism Based on the Immune System. In: Proceedings of the International Symposium on Autonomous Decentralized Systems, Berlin, German 25(3): 231-237.

DOI: 10.1109/isads.1997.590626

[10] Ishiguro A., Watanabe Y., Uchikawa Y (1995).

[11] http: /en. wikipedia. org/wiki/Idiotope#cite_note-titleDorlands_Medical_Dictionary-0 6(5): 28-31 (2012).

[12] Yuan, M.X., Wang S.A., Wu C.Y., Li, K. P (2009) APF - Guided Adaptive Immune Network Algorithm for Robot Path Planning. Frontiers of Computer Science in China 3(2): 247-255.

DOI: 10.1007/s11704-009-0015-5