

浏览全部资源
扫码关注微信
安徽理工大学电气与信息工程学院
Published:2020
移动端阅览
[1]韩涛,黄友锐,周宁亚,等.基于膜计算粒子群优化的FastSLAM算法改进[J].新疆大学学报(自然科学版)(中英文),2020,37(02):156-162.
[1]韩涛,黄友锐,周宁亚,等.基于膜计算粒子群优化的FastSLAM算法改进[J].新疆大学学报(自然科学版)(中英文),2020,37(02):156-162. DOI: 10.13568/j.cnki.651094.651316.2019.09.17.0002.
DOI:10.13568/j.cnki.651094.651316.2019.09.17.0002.
针对FastSLAM算法存在的粒子退化和粒子多样性缺失问题
提出了一种基于膜计算粒子群优化的FastSLAM算法.该算法将膜计算和粒子群优化算法相结合
利用膜计算的并行性、分布式的特点和粒子群优化算法的简单高效的优点
加速调整FastSLAM算法中粒子群的建议分布向全局最优解处收敛
在保证算法局部搜索精度的同时
扩大搜索范围
提高全局搜索的多样性
促使预测粒子更快的朝着真实的机器人位姿状态逼近
减缓粒子退化.最后利用MATLAB平台进行仿真实验.实验结果表明该算法提高了FastSLAM算法的定位精度
同时减少了系统运行时间
效率得到有效提高.
In order to solve the problem of particle degradation and lack of diversity in traditional FastSLAM algorithm
a FastSLAM algorithm based on Particle Swarm Optimization for membrane computing was proposed.This algorithm combines membrane computing with particle swarm optimization(PSO).By taking advantage of the strong parallelism and distributed characteristics of membrane computing and the simple and efficient advantages of PSO
the proposed distribution of particle swarm in FastSLAM algorithm can be adjusted to converge to the global optimal solution
and the local search accuracy of the algorithm can be guaranteed while enlarging.The search range improves the diversity of global search
promotes the predicted particles to approach the real robot position and posture faster
and slows down the particle degradation.Finally
the simulation experiment is carried out on the platform of MATLAB.The experimental results show that the algorithm improves the positioning accuracy of FastSLAM algorithm
reduces the running time of the system
and improves the efficiency effectively.
许鹏程.基于粒子群优化的煤矿井下机器人FASTSLAM算法研究[D].北京:煤炭科学研究总院,2017.XU P C.Research on fast SLAM algorithm of coal mine robot based on particle swarm optimization[D].Beijing:China Coal Research Institute,2017.(in Chinese)
MONTEMARLO M.FastSLAM:A factored solution to the simultaneous localization and mapping problem[C].Proceedings of the AAAI National Conference on Artificial Intelligence,Edmonton,Canada,2002.
LIN M W,YANG C J,LI D J.An Improved Transformed Unscented FastSLAM with Adaptive Genetic Resampling[J].IEEE Transactions on Industrial Electronics,2018,66(5):3583-3594.
LUO J W,QIN S Y.A Fast Algorithm of Simultaneous Localization and Mapping for Mobile Robot Based on Ball Particle Filter[J].IEEE Access,2018,6:20412-20429.
LUO J W,QIN S Y.A Fast Algorithm of SLAM Based on Combinatorial Interval Filters[J].IEEE Access,2018,6:28174-28192.
HAVANGI R,TAGHIRAD H D,NEKOUI M A,et al.A Square Root Unscented FastSLAM With Improved Proposal Distribution and Resampling[J].IEEE Transactions on Industrial Electronics,2014,61(5):2334-2345.
KIM C,SAKTHIVEL R,CHUANG W K.Unscented FastSLAM:arobust and efficient solution to the SLAM problem[J].IEEE Trans-actions on Robotics,2008,24(4):808-820.
MANUEL C,FRANCESCO M.A FastSLAM algorithm based on the unscented filtering with adaptive selective resampling[C].Field and Service Robotics,2008,42:359-368.
CHANG H,YANG W,ZHANG H,et al.An improved FastSLAMusing resmapling based on particle swarm optimization[C].Pro-ceedings of Conference on Industrial Electronics and Applications,2016:229-234.
朱奇光,袁梅,王梓巍,等.机器人球面单径容积FastSLAM算法[J].机器人,2015,37(6):708-717.ZHU Q G,YUAN M,WANG Z W,et al.A Robot Spherical Simplex-Radial Cubature FastSLAM Algorithm[J].Robot,2015,37(6):708-717.(in Chinese)
陈世明,刘俊恺,肖娟.基于引力场优化的Unscented FastSLAM2.0算法[J].控制理论与应用,2018,35(8):1186-1193.CHEN S M,LIU J K,XIAO J.Unscented FastSLAM2.0 algorithm based ongravitational field optimization[J].Control Theory&Applications,2018,35(8):1186-1193.(in Chinese)
张兴义,曾湘祥.膜计算模型、理论及应用研究[J].安徽大学学报(自然科学版),2018,42(3):1-2.ZHANG X Y,ZENG X X.Membrane computing model,theory and Application[J].Journal of Anhui University(Natural Science Edition),2018,42(3):1-2.(in Chinese)
孙辉,邓志诚,赵嘉,等.混合均值中心反向学习粒子群优化算法[J].电子学报,2019,47(9):1809-1818.SUN H,DENG Z C,ZHAO J,et al.Hybrid Mean Center Opposition-Based Learning ParticleSwarm Optimization[J].ACTA ELECTRONICA SINICA,2019,47(9):1809-1818.(in Chinese)
孙远,杨峰,郑晶,等.基于膜计算与粒子群算法的盲源分离方法[J].振动与冲击,2018,37(17):63-71.SUN Y,YANG F,ZHENG J,et al.Blind source separation method based on membrane computing and PSO algorithm[J].Journal of Vibration and Shock,2018,37(17):63-71.(in Chinese)
李月.基于P系统的改进粒子群优化算法及应用[D].济南:山东师范大学,2017.LI Y.Research and Application of the Improved Particle SwarmOptimization Algorithm based on P Systems[D].Jinan:Shandong Normal University,2017.(in Chinese)
陈白帆,蔡自兴,袁成.基于粒子群优化的移动机器人SLAM方法[J].机器人,2009,31(6):513-517.CHEN B F,CAI Z X,YUAN C.Mobile Robot SLAM Method Based on Particle Swarm Optimization[J].Robot,2009,31(6):513-517.(in Chinese)
代嘉惠,许鹏程,李小波.二阶中心差分粒子滤波FastSLAM算法[J].控制理论与应用,2018,35(9):1382-1390.DAI J H,XU P C,LI X B.Second order central difference particle filter FastSLAM algorithm[J].Control Theory&Applications,2018,35(9):1382-1390.(in Chinese)
吴迎国.基于粒子滤波的移动机器人SLAM算法研究[D].绵阳:西南科技大学,2017.WU Y G.Mobile Robot SLAM Algorithm Based on Particle Filter[D].Mianyang:Southwest University of Science and Technology,2017.(in Chinese)
0
Views
181
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621