Ant Colony and Immune Network Algorithm Based on Optimization of Potential Field Method for Path Planning

摘要

Inspired by the mechanisms of idiotypic network hypothesis and ant finding foods,an ant colony and immune network algorithm(AC-INA) for path planning was proposed.Taking the environments surrounding the robot and action strategies as antigens and antibodies respectively,an immune network was constructed by the stimulation and suppression between the antigen and antibody,and the path planning was carried out by the search of ant colony algorithm,which improved the optimal planning of immune network.To further quicken the convergence speed of AC-INA,the path planning results of potential field method were taken as the prior knowledge,and the network was initialized by the vaccine extraction and inoculation.Simulation results indicate that the proposed algorithm(AC-INA) is characterized by high convergence speed,short planning path and self-learning.