[1]白 创,闫 昱,陈 立.室内移动机器人导航系统研究与实现[J].机械与电子,2022,(08):28-32.
 BAI Chuang,YAN Yu,CHEN Li.Research and Implementation of Indoor Mobile Robot Navigation System[J].Machinery & Electronics,2022,(08):28-32.
点击复制

室内移动机器人导航系统研究与实现()
分享到:

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年08期
页码:
28-32
栏目:
设计与研究
出版日期:
2022-08-24

文章信息/Info

Title:
Research and Implementation of Indoor Mobile Robot Navigation System
文章编号:
1001-2257 ( 2022 ) 08-0028-05
作者:
白 创闫 昱陈 立
长沙理工大学物理与电子科学学院,湖南 长沙 410114
Author(s):
BAI Chuang YAN Yu CHEN Li
( School of Physics and Electronic Science , Changsha University of Science and Technology , Changsha 410114 , China )
关键词:
机器人导航传感器数据融合重定位 A * 算法
Keywords:
robot navigation sensor data fusion relocation A* algorithm
分类号:
TP242.6
文献标志码:
A
摘要:
实现了一种低成本高性能室内移动机器人导航系统。针对 Cartographer 算法使用激光雷达数据在室内 Long-Corridor 场景下建图的局部匹配错误导致定位不准的问题,使用扩展卡尔曼滤波融合激光雷达、里程计和惯性测量单元 3 种数据进行位姿估计,得到较为精准的定位,可有效提高建图精度;针对传统 AMCL 算法重定位耗时长的问题,采用基于扫描匹配的重定位方法,通过将当前 Scan 与 Submap 进行匹配,降低了扫描匹配方法的重定位耗时;针对 A * 全局规划算法路径搜索时间长、拐点较多的问题,提出一种改进 A * 算法,通过优化启发函数和增加拐角优化函数,缩短了算法搜索时间,同时去除了冗余拐点。结果表明,重定位耗时减少 80.43% ,改进 A * 算法搜索时间减少 22.79% 。
Abstract:
A low-cost and high-performance indoor mobile robot navigation system is realized.Aiming at the problem of inaccurate positioning caused by the local matching error of the Cartographer algorithm using lidar data to build maps in indoor Long-Corridor scenes , the extended Kalman filter is used to fuse the three data of lidar , odometer and inertial measurement unit for pose estimation.A more accurate positioning can be obtained , which can effectively improve the mapping accuracy ; for the problem that the traditional AMCL algorithm takes a long time to relocate , the relocation method based on scan matching is adopted.By matching the current Scan with the Submap , the relocation time of the scan matching method is reduced.For the problem of long path search time and many inflection points in the A* global planning algorithm , an improved A* algorithm is proposed.By optimizing the heuristic function and adding the corner optimization function , the algorithm search time is shortened , and redundant inflection points are removed.The results show that the relocation time is reduced by 80.43% , and the search time of the improved A* algorithm is reduced by 22.79%.

参考文献/References:

[ 1 ] CHOI B S , LEE J W , LEE J J , et al.A hierarchical algorithm for indoor mobile robot localization using RFID sensor fusion [ J ] .IEEE Transactions on industrial electronics , 2011 , 58 ( 6 ): 2226-2235.
[ 2 ] BERNS K , PUTTKAMER E V.Simultaneous localization and mapping ( SLAM )[ M ] .Wiesbaden : Vieweg Teubner , 2009.
[3 ] 黄通交,侯英岢,倪益华,等 . 基于 ROS 和激光雷达的移动机器人系统设计与实现[ J ] . 机械工程师,2020( 8 ): 46-48 , 51.
[ 4 ] HESS W , KOHLER D , RAPP H, et al.Real-time loop closure in 2D LIDAR SLAM [ C ] ∥2016 IEEE International Conference on Robotics and Automation( ICRA ) .New York : IEEE , 2016 : 1271-1278.
[ 5 ] XU J L , WANG D , LIAO M S , et al.Research of cartographer graph optimization algorithm based on indoor mobile robot [ J ] .Journal of physics : conference series , 2020 , 1651 ( 1 ): 012120.
[ 6 ] WANG W H , CHIEN Y H , CHIANG H H , et al.Autonomous cross-floor navigation system for a ROS based modular service robot [ C ] ∥2019 International Conference on Machine Learning and Cybernetics( ICMLC ), 2019 : 1-6.
[ 7 ] QUIGLEY M , GERKEY B P , CONLEY K , et al. ROS : an open-source robot operating system [ C ] ∥ ICRA Workshop on Open Source Software , 2009 , 3 : 1-6.
[ 8 ] THRUN S.Probabilistic robotics [ J ] .Communications of the ACM , 2005 , 45 ( 3 ): 52-57.

相似文献/References:

[1]胡亚旻.基于比例导引法的带电作业机器人双重避障控制方法[J].机械与电子,2025,(09):26.
 HU Yamin.Dual Obstacle Avoidance Control Method for Live Working Robots Based on Proportional Guidance Method[J].Machinery & Electronics,2025,(08):26.

备注/Memo

备注/Memo:
收稿日期: 2022-03- 08
基金项目:中国 黑山科技合作委员会第 3 届例会交流项目( 3-7 );长沙理工大学“双一流”科学研究国际合作拓展项目( 2019ic18 );柔性电子材料基因工程湖南省重点实验室开放基金( 202005 )
作者简介:白 创 ( 1983- ),男,陕西延安人,博士,讲师,研究方向为计算机视觉、模式识别;闫 昱 ( 1997- ),男,湖南岳阳人,硕士,研究方向为激光 SLAM ,通信作者;陈 立 ( 1996- ),男,湖南张家界人,硕士,研究方向为机器视觉与图像处理。
更新日期/Last Update: 2022-09-02