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Information Technology Journal
Year: 2013 | Volume: 12 | Issue: 9 | Page No.: 1755-1763
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Authors

Mingxin Yuan

Panpan Zhang

Hanyang Li

Yafeng Jiang

Yi Shen

Keywords
Research Article A Novel Mutual-coupled Immune Network Algorithm with the Characteristic of Memory for Mobile Robot Path Planning
Mingxin Yuan
School of Mechanical and Metallurgical Engineering, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
Panpan Zhang
School of Mechanical and Metallurgical Engineering, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
Hanyang Li
School of Mechanical and Metallurgical Engineering, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
Yafeng Jiang
School of Mechanical and Metallurgical Engineering, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
Yi Shen
School of Mechanical and Metallurgical Engineering, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China
To solve the path planning of mobile robots in complex environments, on the basis of Jerne’s idiotypic immune network hypothesis, a novel memory-based Mutual-coupled Immune Network Planning Algorithm (MMCINPA) is proposed. First, according to the obstacle and goal information surrounding the mobile robot, two environment-oriented and goal-oriented immune networks are defined to construct the mutual-coupled immune network for the robot path planning. Then, taking the obstacle and goal as antigen and taking the robot behavior as antibody, the path planning of mobile robot in complicated environments is realized through the antibody selection under the stimulation and suppression among antigen and antibodies. To further improve the planning efficiency of artificial immune network, the historical selected antibody is taken as the immune memory cell and its frequency of usage is added in the calculation of antibody concentration. Compared with Ishiguro’s Simple Mutual-coupled Immune Network Planning Algorithm (SMCINPA), the simulation results in four static environments show that the length of path planned by proposed MMCINPA shortens by about 5.97% and the average turning angle of path by MMCINPA decreases by about 30.11%, which shows the strong planning ability and good flexility of MMCINPA. The path planning result in dynamic environment also verifies the robustness of MMCINPA in uncertain environment.
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