A Composed Global Localization System for Service Robot in Intelligent Space Based on Particle Filter Algorithm and WIFI Fingerprint Localization
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摘要: 针对机器人在没有任何初始位姿先验知识的情况下，通过传感器感知信息确定位姿的全局定位问题，将智能空间技术与 ROS（robot operating system）服务机器人相结合，设计了一种智能空间技术支持下的基于 WIFI 指纹定位和蒙特卡洛粒子滤波定位的复合服务机器人全局定位系统．在该复合定位方法中，首先利用智能空间中的基于 BP（backpropagation）神经网络的 WIFI 指纹定位对机器人进行粗定位，并将估计位置与估计误差发送给 ROS 服务机器人；在粗定位的基础上使用蒙特卡洛粒子滤波算法方法最终获得服务机器人的精确位置．实验结果表明，本文设计的系统实现了 ROS 机器人与智能空间之间的零配置与松耦合，可有效地提高服务机器人全局定位精度，缩短计算迭代时间．Abstract: With the combination of intelligent space technology and ROS (robot operating system) service robot, a composed global localization system for service robot based on Monte Carlo particle filter algorithm and WIFI fingerprint localization is presented. This system can solve the global localization problem in which the initial pose of service robot is unknown and the robot pose is determined according to sensor information. In the composed localization method, robot rough localization is realized firstly using the WIFI fingerprint localization based on BP (backpropagation) neural network under the support of intelligent space technology, and the estimated position and estimation error are sent to the ROS service robot. Based on the rough localization result, Monte Carlo particle filter algorithm is adopted to get precise position of the ROS robot. The experiment results show that the proposed system can realize zero-configuration and loose coupling between ROS service robot and intelligent space, improve precision of global localization of the ROS service robot effectively, and reduce the iteration time.
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[1] 田国会, 李晓磊, 赵守鹏, 等.家庭服务机器人智能空间技术研究与进展[J].山东大学学报:工学版, 2007, 37(5):53-59. Tian G H, Li X L, Zhao S P, et al. Research and development of intelligent space technology for a home service robot[J]. Journal of Shandong University: Engineering Science, 2007, 37(5): 53-59.
[2] Xu X W, Wang X D, Tao Y. Smart space automatic services composition based on program synthesis and QoS model[J]. Journal of Computational Information Systems, 2014, 10(14): 6057-6066.
[3] Dellaert F, Fox D, Burgard W, et al. Monte Carlo localization for mobile robots[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 1999: 1322-1328.
[4] Fox D, Burgard W, Thrun S. Markov localization for mobile robots in dynamic environments[J]. Journal of Artificial Intelligence Research, 1999, 11: 391-427.
[5] 王广奇.基于 RFID 和激光传感器的语义地图更新及机器人定位方法研究[D].济南:山东大学, 2013. Wang G Q. The research of semantic map updates and robot localization based on RFID and laser sensor[D]. Jinan: Shandong University, 2013.
[6] Montemerlo M, Thrun S, Koller D, et al. FastSLAM: A factored solution to the simultaneous localization and mapping problem[C]//18th National Conference on Artificial Intelligence. Piscataway, USA: IEEE, 2002: 593-598.
[7] 邹国柱, 陈万米, 王燕.基于粒子滤波器的移动机器人自定位方法研究[J].工业控制计算机, 2014, 27(10):43-45. Zou G Z, Chen W M, Wang Y. Research on mobile robot localization based on particle filter algorithm[J]. Industrial Control Computer, 2014, 27(10): 43-45.
[8] Thrun S, Burgard W, Fox D. Probabilistic robotics[M]. Cambridge, USA: MIT Press, 2005.
[9] 王勇, 陈卫东, 王景川, 等.面向拥挤环境的移动机器人改进粒子滤波定位[J].机器人, 2012, 34(5):596-603. Wang Y, Chen W D, Wang J C, et al. Improved particle filter localization in crowded environments for mobile robots[J]. Robot, 2012, 34(5): 596-603.
[10] 宋宇, 李庆玲, 康轶非, 等.平方根容积 Rao-Blackwillised 粒子滤波 SLAM 算法[J].自动化学报, 2014, 40(2):357-367. Song Y, Li Q L, Kang Y F, et al. SLAM with square-root cubature Rao-Blackwillised particle filter[J]. Acta Automatica Sinica, 2014, 40(2): 357-367.
[11] Ganganath N, Leung H. Mobile robot localization using odometry and kinect sensor[C]//IEEE International Conference on Emerging Signal Processing Applications. Piscataway, USA: IEEE, 2012: 91-94.
[12] 厉茂海, 洪炳镕, 罗荣华, 等.基于单目视觉的移动机器人全局定位[J].机器人, 2007, 29(2):140-144, 178. Li M H, Hong B R, Luo R H, et al. Monocular-vision-based mobile robot global localization[J]. Robot, 2007, 29(2): 140-144, 178.
[13] 余洪山, 王耀南.基于粒子滤波器的移动机器人定位和地图创建研究进展[J].机器人, 2007, 29(3):281-289,297. Yu H S, Wang Y N. A review on mobile robot localization and mapbuilding algorithms based on particle filters[J]. Robot, 2007, 29(3): 281-289,297.
[14] Quigley M, Conley K, Gerkey B, et al. ROS: An open-source robot operating system[C]//IEEE International Conference on Robotics and Automation: Workshop on Open Source Software. Piscataway, USA: IEEE, 2009: 5-6.
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