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首页> 外文期刊>International Journal of Applied Engineering Research >Implementation of Localization System using Learning Automata based Sensor Fusion in Unmanned Forklifts
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Implementation of Localization System using Learning Automata based Sensor Fusion in Unmanned Forklifts

机译：基于学习自动机的传感器融合在无人叉车中的定位系统的实现

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Unmanned forklifts have great potential to enhance the productivity of material handling in dangerous applications because these forklifts can pick up and deliver loads without an operator or any fixed guide. There are, however, many technical difficulties involved in developing unmanned forklifts including localization, map building, sensor fusion, and control. Recently, the NAV200 positioning system has been used as a localization system, which is the most important component of unmanned forklifts. The NAV200 is a laser measurement system for indoor localization with high accuracy and high precision; however, it has some problems in that it may not operate well when it is required to move fast or has to change its direction at an instant. In order to solve these problems, this paper proposes a learning automata based sensor fusion algorithm with dead reckoning using the kinematics of the unmanned forklift and Kalman filter based prediction using the tendency of movement. To demonstrate the feasibility of the suggested sensor fusion algorithm, its performance is evaluated in computer simulations for various cases.
机译：无人叉车具有极大的潜力，可以提高危险应用中物料搬运的生产率，因为这些叉车无需操作员或任何固定的导向装置就可以拾取和运送货物。但是，开发无人叉车涉及许多技术难题，包括定位，地图构建，传感器融合和控制。最近，NAV200定位系统已被用作定位系统，这是无人叉车的最重要组成部分。 NAV200是一种用于室内定位的激光测量系统，具有高精度和高精度。但是，它存在一些问题，当需要快速移动或必须立即改变方向时，它可能无法正常工作。为了解决这些问题，本文提出了一种基于学习自动机的传感器融合算法，该算法融合了无人驾驶叉车的运动学与航位推算以及基于运动趋势的基于卡尔曼滤波的预测。为了证明所提出的传感器融合算法的可行性，在各种情况下的计算机仿真中对其性能进行了评估。

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