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APF-guided adaptive immune network algorithm for robot path planning

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Abstract

Inspired by the mechanism of Jerne’s idiotypic network hypothesis, a new adaptive immune network algorithm (AINA) is presented through the stimulation and suppression between the antigen and antibody by taking the environment and robot behavior as antigen and antibody respectively. A guiding weight is defined based on the artificial potential field (APF) method, and the guiding weight is combined with antibody vitality to construct a new antibody selection operator, which improves the searching efficiency. In addition, an updating operator of antibody vi-tality is provided based on the Baldwin effect, which results in a positive feedback mechanism of search and accelerates the convergence of the immune network. The simulation and experimental results show that the proposed algorithm is characterized by high searching speed, good convergence performance and strong planning ability, which solves the path planning well in complicated environments.

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Authors and Affiliations

  1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Mingxin Yuan, Sun-an Wang, Canyang Wu & Kunpeng Li

Authors
  1. Mingxin Yuan
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  2. Sun-an Wang
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  3. Canyang Wu
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  4. Kunpeng Li
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Correspondence to Mingxin Yuan.

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Yuan, M., Wang, Sa., Wu, C. et al. APF-guided adaptive immune network algorithm for robot path planning. Front. Comput. Sci. China 3, 247–255 (2009). https://doi.org/10.1007/s11704-009-0015-5

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  • DOI: https://doi.org/10.1007/s11704-009-0015-5

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