基于多传感器融合的移动机器人定位
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摘要
移动机器人定位是进行导航和路径规划的前提和基础。论文提出了一种基于多传感器数据融合的室内移动机器人定位方法。使用基于粒子滤波的蒙特卡洛算法,融合激光雷达、加速度计和里程计等多传感器的信息进行定位,有效改善了传统定位方法因为无法关联底层数据信息或者路面打滑导致定位失败等问题。进而,通过采取基于权值的融合,消除了连续迭代步骤中"跳跃"行为的影响;通过粒子剔除机制,优化了粒子种群,提高算法的执行效率。最后,基于设计的两轮机器人平台,通过仿真和真实环境联合论证,验证了所开发机器人定位方法的有效性。
Mobile robot localization is the premise and basis for navigation and path planning. This paper proposes an indoormobile robot localization method based on multi-sensor data fusion. The Monte Carlo algorithm based on particle filter is used to in-tegrate the information of multi-sensors such as laser radar,accelerometer and odometer to effectively improve the traditional local-ization method because it cannot correlate the underlying data information or the road surface slip causes the localization failure. Fur-thermore,by adopting the weight-based fusion,the influence of the "jumping" behavior in the successive iteration steps is eliminat-ed,the particle culling mechanism optimizes the particle population and improves the execution efficiency of the algorithm. Finally,based on the design of the two-wheeled robot platform,the effectiveness of the developed robot localization method is verified by thejoint demonstration of simulation and real environment.
Mobile robot localization is the premise and basis for navigation and path planning. This paper proposes an indoormobile robot localization method based on multi-sensor data fusion. The Monte Carlo algorithm based on particle filter is used to in-tegrate the information of multi-sensors such as laser radar,accelerometer and odometer to effectively improve the traditional local-ization method because it cannot correlate the underlying data information or the road surface slip causes the localization failure. Fur-thermore,by adopting the weight-based fusion,the influence of the "jumping" behavior in the successive iteration steps is eliminat-ed,the particle culling mechanism optimizes the particle population and improves the execution efficiency of the algorithm. Finally,based on the design of the two-wheeled robot platform,the effectiveness of the developed robot localization method is verified by thejoint demonstration of simulation and real environment.
引文
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[18]Thrun S,Fox D,Burgard W,et al. Robust Monte Carlolocalization for mobile robots[J]. Artificial Intelligence,2001,128(1):99-141.
[19]何珍,楼佩煌,钱晓明,等.多目视觉与激光组合导航AGV精确定位技术研究[J].仪器仪表学报,2017,38(11):2830-2838.HE Zhen,LOU Peihuang,QIAN Xiaoming,et al. Studyon the precise positioning technology of multi-vision vi-sion and laser integrated navigation AGV[J]. ChineseJournal of Scientific Instrument,2017,38(11):2830-2838.
[20]Mandic D P,Obradovic D,Golz M,et al. Data fusionfor modern engineering applications:An overview[C]//International Conference on Artificial Neural Networks.Springer,Berlin,Heidelberg,2005:715-721.
[21]Grisetti G,Stachniss C,Burgard W. Improved techniquesfor grid mapping with rao-blackwellized particle filters[J].IEEE Transactions on Robotics,2007,23(1):34-46.
[2]Lopes A,Rodrigues J,Perdigao J,et al. A new hybridmotion planner:Applied in a brain-actuated roboticwheelchair[J]. IEEE Robotics and Automation Magazine,2016,23(4):82-93.
[3]Oleynikova H,Taylor Z,Siegwart R,et al. Safe local ex-ploration for replanning in cluttered unknown environ-ments for microaerial vehicles[J]. IEEE Robotics and Au-tomation Letters,2018,3(3):1474-1481.
[4]方建博,张福文,刘旭,等.基于ZigBee技术的定位算法分析[J].信息与电脑(理论版),2018(11):58-59.FANG Jianbo,ZHANG Fuwen,LIU Xu,et al. Analysis ofFANG Jianbo,ZHANG Fuwen,LIU Xu,et al. Analysis ofLocation Algorithm Based on ZigBee Technology[J]. Infor-mation and Computer(Theoretical Edition),2018(11):58-59.
[5]周寒宇,王会海,黄相贤.基于UWB三维定位的飞行控制系统[J].电脑与信息技术,2018,26(4):12-16.ZHOU Hanyu,WANG Huihai,HUANG Xiangxian. FlightControl System Based on UWB 3D Positioning[J]. Com-puter and Information Technology,2018,26(4):12-16.
[6]马雪松,赵寒涛,朱明清. CVIRE-RFID室内定位算法[J].自动化技术与应用,2017,36(12):49-53.MA Xuesong, ZHAO Hantao, ZHU Mingqing.CVI-RE-RFID indoor positioning algorithm[J]. AutomationTechnology and Application,2017,36(12):49-53.
[7]张俊龙,刘畅,胡杨,等.基于RSSI的WiFi室内定位技术研究[J].无线互联科技,2018,15(14):16-18.ZHANG Junlong,LIU Chang,HU Yang,et al. Researchon WiFi indoor positioning technology based on RSSI[J].Wireless Interconnect Technology,2018,15(14):16-18.
[8]赵小川,罗庆生,韩宝玲.机器人多传感器信息融合研究综述[J].传感器与微系统,2008,27(8):1-4.ZHAO Xiaochuan,LUO Qingsheng,HAN Baoling. Reviewof Research on Robot Multi-sensor Information Fusion[J]. Journal of Sensors and Microsystems,2008,27(8):1-4.
[9]Thrun S. Probabilistic robotics[J]. Communications of theACM,2002,45(3):52-57.
[10]Jetto L,Longhi S,Venturini G. Development and experi-mental validation of an adaptive extended Kalman filterfor the localization of mobile robots[J]. IEEE Transac-tions on Robotics and Automation,1999,15(2):219-229.
[11]Chen Z,Zou H,Jiang H,et al. Fusion of WiFi,smart-phone sensors and landmarks using the Kalman filter forindoor localization[J]. Sensors,2015,15(1):715-732.
[12]Abreu A. Robot localization from minimalist inertial datausing a hidden Markov model[C]//2014 IEEE Interna-tional Conference on Autonomous Robot Systems andCompetitions. IEEE,2014:247-252.
[13]Fox D,Thrun S,Burgard W,et al. Particle filters for mo-bile robot localization[M]. Sequential Monte Carlo Meth-ods in Practice. Springer,New York,2001:401-428.
[14]张旭,张裕,郑正奇,等.基于核函数与卡尔曼滤波的室内定位方法[J].上海理工大学学报,2016,38(3):287-292.ZHANG Xu,ZHANG Yu,ZHENG Zhengqi,et al. Indoorpositioning method based on kernel function and Kalmanfilter[J]. Journal of University of Shanghai For Scienceand Technology,2016,38(3):287-292.
[15]苗旭东,魏连鑫.改进的粒子群算法的小波支持向量机预警模型[J].上海理工大学学报,2018,40(3):211-216,224.MIAO Xudong,WEI Lianxin. Wavelet Support VectorMachine Early Warning Model Based on Improved Parti-cle Swarm Optimization Algorithm[J]. Journal of Univer-sity of Shanghai For Science and Technology,2018,40(03):211-216,224.
[16]徐荣.基于多传感器信息融合的移动机器人定位算法研究[D].南京:南京大学,2017.XU Rong. Research on mobile robot localization algo-rithm based on multi-sensor information fusion[D].Nanjing:Nanjing University,2017.
[17]Wang P,Chen Z,Zhang Q,et al. A loop closure im-provement method of Gmapping for low cost and resolu-tion laser scanner[C]//Ifac Conference on Manufactur-ing Modelling,Management and Control. 2016.
[18]Thrun S,Fox D,Burgard W,et al. Robust Monte Carlolocalization for mobile robots[J]. Artificial Intelligence,2001,128(1):99-141.
[19]何珍,楼佩煌,钱晓明,等.多目视觉与激光组合导航AGV精确定位技术研究[J].仪器仪表学报,2017,38(11):2830-2838.HE Zhen,LOU Peihuang,QIAN Xiaoming,et al. Studyon the precise positioning technology of multi-vision vi-sion and laser integrated navigation AGV[J]. ChineseJournal of Scientific Instrument,2017,38(11):2830-2838.
[20]Mandic D P,Obradovic D,Golz M,et al. Data fusionfor modern engineering applications:An overview[C]//International Conference on Artificial Neural Networks.Springer,Berlin,Heidelberg,2005:715-721.
[21]Grisetti G,Stachniss C,Burgard W. Improved techniquesfor grid mapping with rao-blackwellized particle filters[J].IEEE Transactions on Robotics,2007,23(1):34-46.