FastSLAM Algorithm Based on Improved Tuna Optimization
YANG Guangyong
WANG Lin
LIU Fukang
XU Tianqi
Abstract：There is a significant gap in the actual distribution of the estimated particle distribution in the FastSLAM algo-rithm,and a large number of particles are needed to improve the accuracy of the algorithm,which will increase the complexity of the calculation.In this paper,the FastSLAM algorithm is optimized with the improved tuna algorithm,first of all,the particle aggre-gation in the original algorithm to the optimal particle aggregation is changed to the average of the particle population,and then the Lévy flight search is introduced when the optimal particle position is obtained to expand the search space of the particles.Finally,the improved tuna optimization algorithm is used to optimize the sampling process of FastSLAM particles to improve the particle quality of the recommended distribution samples.Experimental analysis shows that this method can effectively reduce the error of ro-bot positioning and construction,improve its work efficiency,and can be applied to improve the research of FastSLAM algorithm.
Keywords：FastSLAMproposal distributiontuna optimizationlocalization mapping
Publication Date：2025-06-20
Online Publishing Date：2025-09-23(First online date of this platform, not the publication date of the document)
Pages：6( 1533-1538 )