[1]伍永健,陈跃东,陈孟元.量子粒子群优化下的RBPF-SLAM算法研究[J].智能系统学报,2018,13(05):829-835.[doi:10.11992/tis.201705006]
 WU Yongjian,CHEN Yuedong,CHEN Mengyuan.Research on RBPF-SLAM algorithm based on quantum-behaved particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2018,13(05):829-835.[doi:10.11992/tis.201705006]
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量子粒子群优化下的RBPF-SLAM算法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年05期
页码:
829-835
栏目:
出版日期:
2018-09-05

文章信息/Info

Title:
Research on RBPF-SLAM algorithm based on quantum-behaved particle swarm optimization
作者:
伍永健 陈跃东 陈孟元
安徽工程大学 安徽省电气传动与控制重点实验室, 安徽 芜湖 241000
Author(s):
WU Yongjian CHEN Yuedong CHEN Mengyuan
Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
关键词:
Rao-Blackwellized粒子滤波同时定位与地图构建提议分布量子粒子群优化交叉变异移动机器人机器人操作系统
Keywords:
RBPFsimultaneous localization and map buildingproposed distributionquantum-behaved particle swarm optimizationcrossover and mutationmobile robotROS
分类号:
TP21;TP24
DOI:
10.11992/tis.201705006
摘要:
为了解决传统Rao-Blackwellized粒子滤波(RBPF)存在提议分布精度不高以及重采样过程出现的粒子退化和多样性丢失问题,提出一种量子粒子群(QPSO)优化下的Rao-Blackwellized粒子滤波同时定位与地图构建(RBPF-SLAM)算法。将机器人运动模型和观测模型融合作为混合提议分布,提高提议分布的精度;在重采样过程中引入量子粒子群优化算法更新粒子位姿,根据权值划分粒子种类,引入自适应交叉变异操作,对所得粒子集进行优化、调整,有效地防止粒子退化以及保持粒子的多样性。利用本文算法不仅用MATLAB进行仿真实验,而且结合了旅行家2号移动机器人在机器人操作系统(ROS)上进行实际验证。结果表明,本文算法能以较少粒子数精确估计出机器人的位姿和高精度的地图,误差和运行时间也大大降低了。
Abstract:
The traditional Rao-Blackwellized particle filter (RBPF) is associated with a low distribution accuracy as well as particle degeneracy and loss of diversity during resampling. To solve these problems, a combination of RBPF and simultaneous localization and mapping (RBPF-SLAM) algorithm based on quantum-behaved particle swarm optimization (QPSO) is proposed. A fusion of robot motion model and observation model is proposed as a hybrid distribution to improve accuracy. The QPSO algorithm updates the pose of particles in the resampling process according to the weight measurement of particle type, and an adaptive crossover and mutation operation is introduced to optimize and adjust the particle set to effectively prevent particle degradation and maintain particle diversity. To verify the effectiveness of the improved algorithm, a simulation experiment is performed on MATLAB, as well as a Voyager-Ⅱ mobile robot in a robot operating system (ROS). The results show that the proposed algorithm can accurately estimate the position and pose of the robot and a high precision map, and error and running time are also greatly reduced.

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备注/Memo

备注/Memo:
收稿日期:2017-05-08。
基金项目:2016年度安徽高校自然科学项目(KJ2016A794);2016年安徽工程大学研究生实践与创新基金项目(Y040116004).
作者简介:伍永健,男,1992年生,硕士研究生,主要研究方向为移动机器人路径规划;陈跃东,男,1956年生,教授,主要研究方向为传感器信号处理、移动机器人定位及导航;陈孟元,男,1984年生,副教授,主要研究方向为嵌入式系统开发、图像处理、传感器信息融合及优化。
通讯作者:伍永健.E-mail:2569513970@qq.com.
更新日期/Last Update: 2018-10-25