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 improved 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%.

[2021-12-05] Printed Article /doc/2021.04.0165/v2