基于粒子群优化的粒子滤波定位方法

为了实现移动机器人精确高效的自定位,提出了基于粒子群优化的粒子滤波定位方法.文章分析了常规粒子滤波定位方法存在的不足之处.将最新观测值融合到采样过程中,并利用粒子群优化算法提高了常规粒子滤波器的预估性能.接下来,建立了系统的概率运动模型和感知模型,并利用粒子群优化粒子滤波方法解决了移动机器人的自定位问题.粒子群优化算法的优化结果使得采样集向后验概率密度分布取值较大的区域运动,从而克服了粒子贫乏问题并且显著地降低了精确定位所需的粒子数.仿真实验表明该算法的有效性.

A localization method for particle-filter based on the optimization of particle swarm

To locate a mobile robot efficiently and accurately,we propose a localization algorithm for the particle- filter based on particle swarm optimization.The drawbacks of generic particle- filter are analyzed.By incorporating the newest observations into the sampling process and using particle swarm optimization,the prediction performance of the generic particle-filter is improved.After that,the probabilistic motion-model and observation-model of the mobile robot are established,and the self-localization problem of the mobile robot is resolved by applying the particle swarm optimization to the particle filter.In this method,through particle swarm optimization,particles are moved to the regions where they have larger values of posterior density function.As a result,the impoverishment of the particle filter is overcome and the number of particles needed for accurate location is reduced dramatically.Simulation experiments show the validity of the proposed method.