本文的主要内容是在基于有线和无线相融合的混合网络中，针对移动机器人多传感器采集数据的性能要求，研究如何提高移动机器人远程通信的质量。
The main content for this paper is to make a research on how to enhance the quality of the remote communication in view of the performance requirement of the robot's multi-sensors data.
当前应用于机器人的多传感器数据融合研究大都集中在机器人自主导航和定位问题上，很少涉及利用机器人进行目标跟踪的研究。
The existing researches of multisensor data fusion applied in robots focus mostly navigation and orientation, and a few re-searches about the object tracking using robots are reported.
因此，移动机器人系统涉及的关键技术包括多传感器数据集成与融合、环境建模、控制体系结构和路径规划以及学习机制等。
So main researches in the field of mobile robot are focus on multisensor integration and fusion, world model, control architecture, path planning and knowledge learning etc.
在距离测量和方向测量方面，本文采用数据融合方法，使各个不同传感器的单模信息融合成总的单模信息，清扫机器人通过这个总的单模信息来寻找节点到墙面的行走方向。
On the aspect of distance and direction measuring, we use Kalman filter, it can translate all different sensor information into single, the robot can search the note and walk close to the wall.
讨论了通过二维激光传感器和全维视觉传感器提取数据融合技术进行移动机器人定位的新方法 。
The paper discusses a new mobile robot localization method by means of fusion of a 2-D laser scanner and omni-directional vision.
讨论了通过二维激光传感器和全维视觉传感器提取数据融合技术进行移动机器人定位的新方法 。
The paper discusses a new mobile robot localization method by means of fusion of a 2-D laser scanner and omni-directional vision.
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