新的室内移动机器人自定位方法

doi:10.11772/j.issn.1001-9081.2015.02.0585

计算机应用 ›› 2015, Vol. 35 ›› Issue (2): 585-589.DOI: 10.11772/j.issn.1001-9081.2015.02.0585

新的室内移动机器人自定位方法

周艳聪¹, 董永峰², 王安娜², 顾军华²

1. 天津商业大学信息工程学院, 天津 300134;
2. 河北工业大学计算机科学与软件学院, 天津 300401

收稿日期:2014-08-20 修回日期:2014-11-16 出版日期:2015-02-10 发布日期:2015-02-12
通讯作者: 董永峰
作者简介:周艳聪(1978-),女,河北饶阳人,副教授,博士,CCF会员,主要研究方向:射频识别、机器人定位、智能信息处理; 董永峰(1977-),男,河北定州人,副教授,博士,CCF会员,主要研究方向:射频识别、智能信息处理; 王安娜(1989-),女,河北保定人,硕士研究生,CCF会员,主要研究方向:机器人定位; 顾军华(1966-),男,河北赵县人,教授,博士,CCF会员,主要研究方向:机器人定位。
基金资助:
天津市自然科学基金面上项目(14JCYBJC15900);天津市自然科学基金重点项目(12JCZDJC21200)。

New self-localization method for indoor mobile robot

ZHOU Yancong¹, DONG Yongfeng², WANG Anna², GU Junhua²

1. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China;
2. School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China

Received:2014-08-20 Revised:2014-11-16 Online:2015-02-10 Published:2015-02-12

摘要/Abstract

摘要：

针对现有室内移动机器人自定位方法中存在的定位精度不高,随时间积累定位误差增大,复杂室内环境下信号存在多径效应和非视距效应等问题,提出了一种基于蒙特卡罗定位(MCL)的新的移动机器人自定位方法。首先,通过分析基于无线射频识别(RFID)技术的移动机器人自定位系统,建立机器人运动模型;然后,通过分析基于接收信号强度指示(RSSI)的移动机器人自定位系统,提出机器人移动过程的观测模型;最后,针对粒子滤波定位执行效率不高的问题,提出粒子剔除策略和依据粒子方位赋予粒子权值策略,提高系统的定位精度和执行效率。仿真实验表明,机器人在移动过程中的自定位误差在X轴和Y轴方向上为3 cm,传统定位算法误差为6cm,新算法定位精度提高近1倍,且算法具有很好的鲁棒性。

关键词: 机器人自定位, 射频识别, 极大似然定位方法, 蒙特卡罗定位, 粒子滤波

Abstract:

Aiming at the problems of the current self-localization algorithms for indoor mobile robot, such as the low positioning accuracy, increasing positioning error with time, the signal's multipath effect and non-line-of-sight effect, a new mobile robot self-localization method based on Monte Carlo Localization (MCL) was proposed. Firstly, through analyzing the mobile robot self-localization system based on Radio Frequency IDentification (RFID), the robot motion model was established. Secondly, through the analysis of the mobile robot positioning system based on Received Signal Strength Indicator (RSSI), the observation model was put forward. Finally, in order to improve the computing efficiency of particle filter, the particle culling strategy and particle weight strategy considering orientation of the particles were given, to enhance the positioning accuracy and the execution efficiency of the new positioning system. The position errors of the new algorithm were about 3 cm in both the X direction and the Y direction, while position error of the traditional localization algorithm in the X direction and the Y direction were both about 6 cm. Simulation results show that the new algorithm doubles the positioning accuracy, and has good robustness.

Key words: robot self-localization, Radio Frequency IDentification (RFID), maximum likelihood positioning method, Monte Carlo Localization (MCL), particle filter

中图分类号:

TP391.9
TP18

周艳聪, 董永峰, 王安娜, 顾军华. 新的室内移动机器人自定位方法[J]. 计算机应用, 2015, 35(2): 585-589.

ZHOU Yancong, DONG Yongfeng, WANG Anna, GU Junhua. New self-localization method for indoor mobile robot[J]. Journal of Computer Applications, 2015, 35(2): 585-589.

参考文献

[1] ZHANG L, PAN C, LIU Z, et al. A new method of dead reckoning for differential driver mobile robots [J]. Journal of Hefei University of Technology: Natural Science Edition, 2010, 33(11): 1605-1608. (张利, 潘承毅, 刘征宇, 等. 差动驱动机器人的一种航迹推算定位方法 [J]. 合肥工业大学学报:自然科学版, 2010, 33(11): 1605-1608.)
[2] XIA G, YANG C, CAI S. System design and implementation of tele-autonomous mobile robot [J]. Journal of Computer Applications, 2011, 31(11): 3129-3131. (夏桂华,杨晟,蔡成涛. 遥自主移动机器人系统设计及实现 [J]. 计算机应用,2011,31(11):3129-3131.)
[3] GUTMANN J-S, FONG P, CHIU L, et al. Challenges of designing a low-cost indoor localization system using active beacons [C]//TePRA 2013: Proceedings of the 2013 International Conference on Technologies for Practical Robot Applications. Piscataway: IEEE, 2013: 1-6.
[4] LIU T, ZHANG W, GU J, et al. A laser radar based mobile robot localization method [C]//ROBIO 2013: Proceedings of the 2013 International Conference on Robotics and Biomimetics. Piscataway: IEEE, 2013: 2511-2514.
[5] ZHANG Q, LI L, LIU Y. Simulation robot localization method based on Kalman filter [J]. Computer Simulation, 2013, 30(12): 317-320. (张青, 李龙澍, 刘跃. 基于卡尔曼滤波的仿真机器人定位方法 [J].计算机仿真,2013,30(12):317-320.
[6] ZENG J, WANG B, XIE H. Improved Kalman filter in positioning system for autonomous mobile robot [J]. Application Research of Computers, 2011, 28(5): 1710-1712. (曾健平,王保同,谢海情.自主移动机器人定位系统中Kalman滤波算法改进 [J].计算机应用研究,2011,28(5):1710-1712.)
[7] WANG X, YANG X. Particle filter SLAM algorithm based on hybrid information filter [J]. Application Research of Computers, 2013, 30(7): 1988-1990. (王晓华,杨幸芳.基于混合信息滤波的粒子滤波SLAM算法 [J].计算机应用研究,2013,30(7):1988-1990.)
[8] XU C, FU L, MAO J, et al. Improved PF algorithm for WSNs for mobile robot localization and tracking [J]. Transducer and Microsystem Technologies, 2014, 33(5): 48-51. (徐驰,付丽霞,毛剑琳,等.WSNs的改进PF算法对移动机器人的定位跟踪 [J].传感器与微系统,2014,33(5):48-51.)
[9] ALVES P, COSTELHA H, NEVES C. Localization and navigation of a mobile robot in an office-like environment [C]//ARS 2013: Proceedings of the 2013 13th International Conference on Autonomous Robot Systems. Piscataway: IEEE, 2013: 1-6.
[10] HSU C-C, KUO C-J, KAO W-C. Improved Monte Carlo localization with robust orientation estimation for mobile robots [C]//SMC 2013: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway: IEEE, 2013: 3651-3656.
[11] ZHANG H, CHEN J, ZHANG K. Reliable and efficient RFID-based localization for mobile robot [C]//ROSE 2013: Proceedings of the 2013 IEEE International Symposium on Robotic and Sensors Environments. Piscataway: IEEE, 2013: 184-189.
[12] YANG X, YE A, LING Y. Indoor localization algorithm based on threshold classification and signal strength weighting [J]. Journal of Computer Applications, 2013, 33(10): 2711-2714. (杨小亮,叶阿勇,凌远景. 基于阈值分类及信号强度加权的室内定位算法 [J].计算机应用,2013,33(10):2711-2714.)
[13] ZHOU N, ZHAO X, TAN M. RSSI-based mobile robot navigation in grid-pattern wireless sensor network [C]//CAC 2013: Proceedings of the 2013 National Conference on Chinese Automation Congress. Piscataway: IEEE, 2013: 497-501.
[14] LEE H J, KIM M S, LEE M C. Technique to correct the localization error of the mobile robot positioning system using an RFID [C]//SICE 2007: Proceedings of the 2007 Annual Conference on Instrumentation and Control Engineering. Piscataway: IEEE, 2007: 1506-1511.
[15] WANG D, LAN Y, REN F, et al. Indoor mobile-robot localization system based on active RFID [J]. Journal of Tsinghua University: Science and Technology, 2010, 50(5): 673-676. (王殿君,兰云峰,任福君,等.基于有源RFID的室内移动机器人定位系统 [J].清华大学学报:自然科学版,2010,50(5):673-676.)

新的室内移动机器人自定位方法

New self-localization method for indoor mobile robot

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