基于头脑风暴算法的FastSLAM 2.0算法

针对FastSLAM2.0算法粒子权值退化与粒子多样性丧失导致机器人定位建图精度下降的问题,提出了基于头脑风暴算法改进FastSLAM2.0算法.通过头脑风暴算法替换FastSLAM2.0算法重采样过程,首先将重要性采样后的粒子权值作为头脑风暴算法中个体评判的适度值,根据适度值大小差异完成K-means聚类操作;其次对聚类后的集合进行变异操作,并取消头脑风暴算法中个体选择操作,从而实现改进头脑风暴算法替代FastSLAM2.0算法重采样过程,缓解粒子的贫化现象,增加粒子多样性,最终实现对机器人定位建图精度的提升.在机器人定位建图实验中,对比经典FastSLAM2.0算法和基于遗传算法改进FastSLAM2.0算法,提出的算法定位精度最高,相较于经典FastSLAM2.0算法,提出算法定位精度提升了63％,稳定性提升了55％.

FastSLAM 2.0 algorithm based on brain storm optimization

Aiming at the problem that the degradation of particle weight and the loss of particle diversity in FastSLAM 2.0 algorithm led to the decline of robot positioning and mapping accuracy, this paper proposed an improved FastSLAM 2.0 algorithm based on brain storm optimization. Firstly, it took the particle weight after importance sampling as the moderation value of individual evaluation in the brainstorming algorithm, and completed K-means clustering operation according to the difference of the moderation value. Secondly, it mutated the set after clustering to cancel the individual selection operation in the brainstorm algorithm. In this way, the improved brainstorm algorithm could replace the resampling process of particle filter, effectively alleviate the particle dilution phenomenon, increase the particle diversity, and finally improve the positioning and mapping accuracy of the rescue robot. In the robot positioning mapping experiment, compared with the classic FastSLAM 2.0 algorithm and the improved FastSLAM 2.0 algorithm based on genetic algorithm, the proposed algorithm has the highest positioning accuracy. Compared with the classic FastSLAM 2.0 algorithm, the positioning accuracy of the proposed algorithm is improved by 63%, and the stability is improved by 55%.