针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM ̄CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM ̄CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11%,比EKF算法提高了65.81%。
Targeting localization of mobile robots, an optimized localization algorithm interweaved with Gaussian Mixture Consider Kalman Filter(GM ̄CKF)under Wireless Sensor Network(WSNs)is proposed in this article. The process is as follows. Combining the observation of mobile robots by WSNs,the observation of environmental charac ̄teristics by mobile robots themselves together with the amount of self control,a date fusion is conducted. With the aid of GM ̄CKF algorithm featuring threshold discriminant analysis and selective Gaussian Segmentation,the robots’ estimated positions are predictively rectified so that calculated space dimension could be decreased and locational preciseness could be increased. The simulations are carried out to demonstrate that the method promoted in this article produces better preciseness which is 39.11% higher than that of the well ̄performed CKF and 65.81% higher than that of EKF,compared with the old self ̄localization one.