基于相对观测量的多机器人定位

研究了多机器人队列利用相对观测信息在未知环境中进行同时定位的问题。当队列中某个机器人观测到另外一个或几个机器人时,利用这些信息来同时更新整个队列的位置及协方差矩阵,也即整个队列共享所获得的观测,来得到更精确的位置估计。每个机器人都携带内部及外部传感器,内部传感器感知机器人自身的运动,外部传感器能提供机器人之间的相对观测量,如相对距离和相对方位。利用扩展卡尔曼滤波(EKF)算法融合内部及外部传感器信息,对多机器人队列进行同时定位;并对不同的观测量及机器人个数进行了仿真分析,给出了不同情况下的滤波器结构,研究比较了它们的定位精度。仿真结果表明,利用机器人之间的相对观测信息,可以显著提高定位精度。

Simultaneous Localization for Multi-robot Based on Relative Observations

The problem of multi-robot simultaneous localization in an unknown environment based on relative observation was studied.Each robot was equipped with proprioceptive sensors and exteroceptive sensors.Exteroceptive sensors provided relative observations between robots,such as relative distance and bearing.Considering the nonlinear problem,we used extended Kalman filter to fuse proprioceptive and exteroceptive data.Different relative observations and the number of the robots were studied and simulated.The filter structure for each case was presented and the performances of the algorithm were compared as to the accuracy and other aspects.Simulation results prove that the location accuracy has been improved effectively by using relative observations among robots.