基于ISSA-FastSLAM的移动机器人定位与建图  

Localization and Mapping of Mobile Robot Based on ISSA-FastSLAM

作　　者：杨光永[1] 蔡艳 吴大飞 徐天奇[1] YANG Guangyong;CAI Yan;WU Dafei;XU Tianqi(School of Electrical Information Engineering,Yunnan Minzu University,Kunming 650000,China)

机构地区：[1]云南民族大学电气信息工程学院,昆明650000

出　　处：《组合机床与自动化加工技术》2023年第6期68-71,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(61761049,61261022)。

摘　　要：针对传统FastSLAM算法需要大量粒子提高SLAM精度以及多样性缺失的问题,提出了一种基于改进SSA(麻雀算法)优化的FastSLAM算法。首先,在预测粒子集时加入机器人最新时刻的观测信息并通过改进SSA计算粒子适应度值,为避免SSA陷进局部最优,将计算结果较差的粒子进行混沌初始化;其次,通过改进SSA分工协作、扩大搜索空间的特性更新预测粒子集,增加粒子多样性;最后,当最优个体更新位置时依据变异率进行变异操作,根据改进SSA获取的最优解调整粒子集的提议分布,使预测粒子集在权重计算前就更逼近机器人真实位置,以此提高估计精度。仿真实验结果表明,ISSA-FastSLAM算法较FastSLAM、SSA-FastSLAM算法相比,其位姿与路标估计精度更高且鲁棒性更佳。Aiming at the problem that the traditional FastSLAM algorithm needs a lot of particles to improve the accuracy of SLAM and the lack of diversity,a FastSLAM algorithm based on improved SSA(sparrow algorithm)optimization is proposed.First,when predicting the particle set,the observation information of the robot at the latest time is added and the particle fitness value is calculated by improving the SSA.In order to avoid the SSA from falling into the local optimum,the particles with poor calculation results are chaotic initialized;Secondly,by improving the division of labor and cooperation of SSA and expanding the search space,the predicted particle set is updated to increase the diversity of particles;Finally,when the optimal individual updates the position,the mutation operation is carried out according to the mutation rate,and the proposed distribution of the particle set is adjusted according to the optimal solution obtained by the improved SSA,so that the predicted particle set is closer to the real position of the robot before the weight calculation,so as to improve the estimation accuracy.The simulation results show that ISSA-FastSLAM algorithm has higher accuracy and better robustness than FastSLAM and SSA-FastSLAM algorithms.

关 键 词：同时定位与建图 FASTSLAM算法 提议分布 麻雀算法 

分 类 号：TH165[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]