Mobile robot path planning using polyclonal-based artificial immune network

  • Authors:

  • Lixia Deng;Xin Ma;Jason Gu;Yibin Li

  • Affiliations:

  • School of Control Science and Engineering, Shandong University, Jinan, Shandong, China;School of Control Science and Engineering, Shandong University, Jinan, Shandong, China;School of Control Science and Engineering, Shandong University, Jinan, Shandong, China and Department of Electrical and Computer Engineering, Dalhousie University, Halifax, Canada;School of Control Science and Engineering, Shandong University, Jinan, Shandong, China

  • Venue:
  • Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
  • Year:
  • 2013

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Abstract

Polyclonal based artificial immune network (PC-AIN) is utilized formobile 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.