摘要
针对助老机器人服务的特殊性,决定了其工作环境的开放性,而在开放环境中要求精确定位,保证助老机器人安全工作。影响机器人精确定位的因素主要是,未知环境下路标特征的提取易受环境影响,所建模型的噪声特性未知。为了提高助老机器人的精确定位,提出一种采用未知路标的多传感器信息融合的无迹卡尔曼滤波的定位方法,并采用改进的SageHusa自适应滤波算法对其未知噪声方差阵进行估计。仿真结果表明,在未知路标下,系统噪声曲线稳定控制在极小范围内,证明了Sage-Husa自适应UKF算法是有效性,为助老机器人的广泛使用提供了理论基础。
The characteristic of the service provided by service robots for the elderly has determined the openness of its work circumstances ; and the accurate positioning of the robot in these kinds of circumstances is the main factor of its safety work. There are two main factors that influence the robot＇ s accurate positioning. Firstly, it is hard to obtain the characteristics of the road signs in the unknown conditions ; secondly, the noise characteristics of the model are also unknown. In order to improve the accurate positioning of the robot, the multi-sensor information fusion of Unscented Kalman Filter（UKF） positioning method in the unknown road signs conditions was proposed. Meanwhile, we adopted the improved Sage-Husa adaptive filter method to estimate the unknown noise covariance matrix. Simulation results show that system noise curve has been confined to a small range; it also demonstrates the effectiveness of the Sage-Husa adaptive UKF method and provides a theoretical basis for the wide use of the service robots for the elderly.
出处
《计算机仿真》
CSCD
北大核心
2016年第7期363-368,共6页
Computer Simulation
