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A new approach using sensor data fusion for mobile robot navigation

机译:使用传感器数据融合进行移动机器人导航的新方法

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To fully utilize the information from the sensors, this paper proposes a new sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable an accurate measurement. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Note that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. The theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, STSF (Space and Time Sensor Fusion) scheme is applied to the navigation of a mobile robot in an unstructured environment, as well as in structured environment, and the experimental results show the performance of the system.
机译:为了充分利用来自传感器的信息,本文提出了一种新的传感器融合技术,其中可以将先前时刻的数据集正确转换并融合到当前数据集中,以实现准确的测量。探索未知环境是新一代移动服务机器人的一项重要任务。移动机器人可以借助于诸如声纳感测系统或视觉感测系统的多个监视系统来导航。注意,在常规融合方案中,测量仅取决于当前数据集。因此,需要更多的传感器来测量某个物理参数或提高测量精度。但是,在这种方法中,不是将更多的传感器添加到系统中,而是存储数据集的时间顺序并将其用于精确测量。通过实例说明了理论基础,并通过仿真和实验证明了其有效性。新提出的STSF(空间和时间传感器融合)方案应用于非结构化环境以及结构化环境中的移动机器人导航,实验结果表明了该系统的性能。

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