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首页> 外文会议>Conference on Artificial Intelligence and Robotics;Asia-Pacific International Symposium >Localization and Navigation Omni-directional Robots based on Sensors Fusion and Particle Filter
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Localization and Navigation Omni-directional Robots based on Sensors Fusion and Particle Filter

机译：基于传感器融合和粒子滤波的定位与导航全向机器人

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One of the main challenges in autonomous mobile robots is robust and accurate navigation of a robot in its environment. Obviously, this is conceivable when the precise position of the robot in the environment is needed to get by a control algorithm before navigation. Due to the lack of coverages for using one sensor in environmental conditions, multi-sensor data fusion is one of the successful solutions to overcome these problems. This paper presents an efficient localization and navigation of an industrial robot using integration of multi sensors. To do this, the relative position of the robot is obtained by passing normalized data of laser scanner, compass and encoder sensors to a Kalman filter. Due to relative and accumulated errors in the Kalman filter, exact position is obtained by applying a particle filter on environment's map. The proposed method has been run on a real industrial robot in an industrial environment according to RoboCup rules. The experimental results achieved in mean error
机译：自主移动机器人的主要挑战之一是机器人在其环境中的鲁棒且准确的导航。显然，当需要在导航之前通过控制算法获取机器人在环境中的精确位置时，这是可以想到的。由于缺乏在环境条件下使用一个传感器的覆盖范围，因此多传感器数据融合是克服这些问题的成功解决方案之一。本文提出了通过集成多传感器对工业机器人进行有效的定位和导航。为此，通过将激光扫描仪，罗盘和编码器传感器的标准化数据传递到卡尔曼滤波器来获得机器人的相对位置。由于卡尔曼滤波器中存在相对误差和累积误差，因此可以通过在环境图上应用粒子滤波器来获得准确的位置。根据RoboCup规则，该方法已在工业环境中的真实工业机器人上运行。实验结果均值误差

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