Computer and Modernization ›› 2022, Vol. 0 ›› Issue (02): 7-12.

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Improved Immune Particle Swarm Optimization Algorithm for Automatic Parallel Parking Based on Cubic Spline Interpolation#br#

  

  1. (1. Test Department, CRRC Dalian R&D Co. Ltd., Dalian 116041, China; 
    2. School of Automation and Electrical Engineering, Dalian Jiaotong University, Liaoning Dalian, 116028, China; 
    3. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
    4. College of Engineering, Inner Mongolia University for Nationalities, Inner Mongolia Tongliao 028000, China; 
    5. School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China;  
    6. School of Mechanical and Electrical Engineering, Jiangxi New Energy Technology Institute, Jiangxi Xinyu 338004, China; 
    7. Inner Mongolia Minzu University Key Laboratory of Intelligent Manufacturing Technology, Inner Mongolia Tongliao 028000, China)
  • Online:2022-03-31 Published:2022-03-31

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Abstract: It is difficult to obtain the smooth, accurate and optimal parking trajectory by using traditional automatic parallel parking optimization algorithm. For obtaining ideal optimal parking target trajectory, combined with the intelligent automatic parking theory, an automatic parallel parking method based on cubic spline interpolation is proposed. In order to improve the optimization performance for automatic parallel parking optimization algorithm effectively, an immune improved particle swarm optimization algorithm (IIPSO) based on cubic spline interpolation is proposed for choosing an appropriate parking position reference points by using shortest parking trajectory as optimization target. Firstly, for enhancing the global search performance and convergence velocity of particle swarm optimization (PSO), an adaptive mutation strategy is introduced. Secondly, an immune strategy is introduced to improve the global optimization ability of particle swarm optimization. The simulation results of test functions and the practical example of automatic parking indicate that the IIPSO algorithm proposed in this paper has better optimization precision and faster convergence speed.

Key words: cubic spline interpolation, automatic parallel parking, particle swarm optimization (PSO), adaptive mutation, immune