Robot localization method using inversion set estimation in sensor networks
- 国际学院－已发表论文 
针对大型协作环境中移动机器人的全局定位问题,提出根据机器人车载传感器、环境传感器以及其他机器人的实时数据估计移动机器人的位置。首先,提出的方法整合大量不同类型传感器;然后,考虑了测量值数量可变、通用测角测量、受容错约束的测量统计知识等约束条件,将非线性边界误差估计问题看做一种反演集合;最后,处理特定类型的异常值和不精确环境下的模型误差。完成了误差和异常值的处理,就基本上获得了定位图,解决了移动机器人的定位问题。提出的方法利用实物实验进行验证。测试区域装备有多个传感器、固定在墙顶部的摄像机以及位于机器人上的可见标记。实验结果表明,提出的方法在协作环境中具有明显优势,处理异常值更加可靠。Concerning the global localization problem of mobile robots operating in large and cooperative environments, this paper proposed that the position of a robot was estimated in the environment using real-time data from the robot on-board sensors, the environmental sensors and other robots. Firstly, the proposed method integrated a large number of different types of sensors. Then, the constraint conditions such as the variable number of measurements, the general measurement of the angle measurement, the measurement and statistical knowledge affected by fauh tolerance constraints were considered. The problem of nonlinear boundary error estimation was being as an inverse set. Outliers of the specific type and model error in the impre- cise environment could be done finally. When it completed the processing of the error and outliers, the location map was ob- tained basically, which solved the location problem of the mobile robots. The proposed method was verified by physical experi- ment. The test area was equipped with a plurality of sensors, a camera fixed to the top of the wall and a visible mark on the robot. The experimental results show that the proposed method has obvious advantages in the cooperative environment, and it is more reliable to deal with outliers.