基于EKF和PF的多机器人协同定位技术
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摘要
无论对于单机器人还是多机器人系统,定位能力是其完成各项任务的前提条件。针对常用定位技术难以满足移动机器人群体协同定位精度高、实时性强等要求,提出了一种基于扩展卡尔曼滤波(EKF)和粒子滤波(PF)的混合定位技术。首先,对机器人进行运动建模,然后分别讨论基于扩展卡尔曼滤波和粒子滤波的机器人协同定位的基本原理、优缺点,在此基础上提出基于扩展卡尔曼滤波和粒子滤波相结合的协同定位方法。结果表明,该算法在满足一定条件下可有效解决定位精度与运算量之间的矛盾,可实现在初始条件未知或存在较大误差情况下多机器人快速、精确地协同定位。
The positioning capacity is a prerequisite to complete the tasks,no matter for single-robot or multi-robots systems. It is difficult for common location technique to meet the requirements of mobile robot groups in high co-localization accuracy and strong real-time. A hybrid location technique based on Extended Kalman Filter(EKF)and Particle Filter(PF)is proposed. The multi-robots are motion modeling,then the basic principle of robot co-localization based on EKF or PF and their advantages and disadvantages are discussed respectively. On this basis,another co-localization based on the combination of EKF and PF is proposed. The experiment result shows that this method is effectively in solving the contradiction between positioning accuracy and calculation under some condition. And when the initial condition is unknown or the errors is large,the multi-robots co-localization can also be rapidly and precisely.
The positioning capacity is a prerequisite to complete the tasks,no matter for single-robot or multi-robots systems. It is difficult for common location technique to meet the requirements of mobile robot groups in high co-localization accuracy and strong real-time. A hybrid location technique based on Extended Kalman Filter(EKF)and Particle Filter(PF)is proposed. The multi-robots are motion modeling,then the basic principle of robot co-localization based on EKF or PF and their advantages and disadvantages are discussed respectively. On this basis,another co-localization based on the combination of EKF and PF is proposed. The experiment result shows that this method is effectively in solving the contradiction between positioning accuracy and calculation under some condition. And when the initial condition is unknown or the errors is large,the multi-robots co-localization can also be rapidly and precisely.
引文
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[9]张诗桂,朱立新,赵义正.粒子滤波算法研究进展与展望[J].自动化技术与应用,2010(6):1-9.
[10]王炎庆.基于卡尔曼滤波器的神经元干细胞序列图像中活跃细胞的追踪[D].哈尔滨:哈尔滨工程大学,2010.
[2]邵金鑫.基于多传感器信息融合的多机器人协同定位技术研究[D].长沙:国防科技大学,2006.
[3]刘显荣.传感器融合在多机器人合作定位中的应用[J].科技通报,2005(2):277-281.
[4]付梦印,邓志红,张继伟.Kalman滤波理论及其在导航系统中的应用[M].北京:科学出版社,2003.
[5]TESLICL,SKRJANC I,KLANCAR G.EKF-based localization of a wheeled mobile robot in structured environment[J].Journal of Intelligent and Robotic Systems,2011,62(2):187-203.
[6]周翟和,刘建业,赖际舟.粒子滤波的改进优化算法在组合导航系统中的应用[J].中国惯性技术学报,2010(6):711-715.
[7]张淼,胡建旺,周云峰,等.改进粒子滤波算法的比较[J].电光与控制,2009(2):30-32.
[8]张弦.基于改进粒子滤波算法的移动机器人定位[D].北京:北京邮电大学,2010.
[9]张诗桂,朱立新,赵义正.粒子滤波算法研究进展与展望[J].自动化技术与应用,2010(6):1-9.
[10]王炎庆.基于卡尔曼滤波器的神经元干细胞序列图像中活跃细胞的追踪[D].哈尔滨:哈尔滨工程大学,2010.