基于秃鹰搜索算法优化的FastSLAM算法

FastSLAM Algorithm Optimized Based on Bald Eagle Search Algorithm

针对FastSLAM算法存在的粒子退化和多样性丢失导致定位与建图精度下降的问题,提出了一种基于秃鹰搜索算法(BES)优化的FastSLAM算法(BES-FastSLAM)。BES-FastSLAM算法借助其特有的搜索机制,可以在粒子初始化阶段引入更丰富的粒子,增加粒子多样性,帮助FastSLAM算法跳出局部最优解的束缚,增强全局搜索能力。同时,BES-FastSLAM算法的速度和位置更新机制可以动态调整粒子群的速度和位置,使FastSLAM算法更快地收敛到全局最优解。仿真实验验证了BES-FastSLAM算法相较于FastSLAM算法拥有更高的计算效率和定位精度。

In order to solve the problems of particle degradation and loss of diversity in FastSLAM algorithm that lead to decreased accuracy in localization and mapping,a FastSLAM algorithm(BES-FastSLAM)optimized on the basis of the bald eagle search(BES)algorithm is proposed.With its unique search mechanism,the BES-FastSLAM algorithm can introduce richer particles during the particle initialization phase to increase the particle diversity,which helps the FastSLAM algorithm to break free from the constraints of the local optima,enhancing global search capabilities.Meanwhile,the speed and position update mechanism of the BES-FastSLAM algorithm can dynamically adjust the speed and position of the particle swarm,enabling the FastSLAM algorithm to converge to the global optimal solution faster.Simulation experiments have verified that the BES-FastSLAM algorithm has higher computational efficiency and localization accuracy compared to the FastSLAM algorithm.