Avoidance of singular localization environment using model predictive control for mobile robots
Avoidance of singular localization environment using model predictive control for mobile robots
复制标题
使用移动机器人模型预测控制避免奇异定位环境
DOI：
10.1109/ascc.2017.8287632
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
K. Sekiguchi
中科院分区：
文献类型：
--
作者：
Masaki Koizumi;K. Nonaka;K. Sekiguchi
定位是实现自主式移动的机器人安全运动的重要环节。在本文中，我们提出了一个模型预测控制，防止车辆陷入一个奇异的定位环境与较少的功能。我们假设使用激光测距仪（LRF）作为传感器，以获得周围环境的二维点云数据，并应用地图匹配的方法进行定位。利用Fisher信息矩阵可以计算出定位误差的协方差矩阵。该协方差矩阵的最大特征值被用作定位不确定性的指标。然后，我们提取区域具有较大的不确定性的定位，以构建禁止区域。机器人避免奇异环境和区域的估计精度显着较低，通过考虑禁止的区域表示为不等式约束模型预测控制（MPC）。我们所提出的方法的有效性，通过数值模拟，模拟奇异的环境表明走廊或一个大的地板，只有退化的信息是可用的。
Localization is important to achieve safety motion of autonomous mobile robots. In this paper, we propose a model predictive control which prevents vehicles from falling into a singular localization environment with less features. We assume to use a laser range finder (LRF) as a sensor to obtain two dimensional point-cloud data of the surrounding environment and apply map-matching method for localization. We can calculate a covariance matrix of localization error using Fisher information matrix. The maximum eigenvalue of this covariance matrix is used as an index of uncertainty of localization. Then, we extract regions having large uncertainty for localization to construct prohibited regions. The robot avoids singular environments and regions with significantly low estimation accuracy by considering the prohibited regions represented as inequality constraints for model predictive control (MPC). We show the effectiveness of the proposed method through numerical simulations which simulate singular environments indicating a corridor or a large floor where only degenerated information is available.