基于三次样条插值的自动入库泊车免疫粒子群改进算法

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

传统自动入库泊车轨迹优化算法不易寻到光滑、精确且优化的泊车轨迹。结合智能自动入库泊车原理,本文提出一种基于三次样条插值的自动入库泊车方法,从而获得理想优化的泊车参考轨迹。为了有效地提升自动入库泊车轨迹寻优算法的性能,以泊车轨迹最短作为优化目标来选定一组合适的泊车位置参考点,在三次样条插值的基础上,又提出一种免疫粒子群改进算法。首先,为提升算法全局搜索性能和收敛速度,引入自适应变异策略;然后,引入免疫机制来有效提升其全局优化能力。测试函数及自动入库泊车实际算例的仿真结果表明,所提出的自动入库泊车免疫粒子群改进算法具有更高的寻优精度和较快的收敛速度。

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.