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Journal of Control Science and Engineering
Volume 2013, Article ID 416715, 13 pages
Research Article

Mobile Robot Path Planning Using Polyclonal-Based Artificial Immune Network

1School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
2Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada B3J 2X4

Received 14 June 2013; Accepted 14 August 2013

Academic Editor: Fei Liu

Copyright © 2013 Lixia Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Polyclonal based artificial immune network (PC-AIN) is utilized for mobile robot path planning. Artificial immune network (AIN) has been widely used in optimizing the navigation path with the strong searching ability and learning ability. However, artificial immune network exists as a problem of immature convergence which some or all individuals tend to the same extreme value in the solution space. Thus, polyclonal-based artificial immune network algorithm is proposed to solve the problem of immature convergence in complex unknown static environment. Immunity polyclonal algorithm (IPCA) increases the diversity of antibodies which tend to the same extreme value and finally selects the antibody with highest concentration. Meanwhile, immunity polyclonal algorithm effectively solves the problem of local minima caused by artificial potential field during the structure of parameter in artificial immune network. Extensive experiments show that the proposed method not only solves immature convergence problem of artificial immune network but also overcomes local minima problem of artificial potential field. So, mobile robot can avoid obstacles, escape traps, and reach the goal with optimum path and faster convergence speed.