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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Navigation for Indoor Robot: Straight Line Movement via Navigator
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Navigation for Indoor Robot: Straight Line Movement via Navigator

机译:室内机器人导航:通过导航器直线移动

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Due to the need of zigzag overlay strategy, long-term linear motion is essential for sweep robot. However, the existing indoor sweep robot navigation algorithm has many problems; for instance, algorithm with high complexity demands high hardware performance and is incapable of working at night. To overcome those problems, in this paper, a new method for indoor robot Straight Line Movement via Navigator (SLMN) is proposed to ensure long linear motion of robot with an acceptable error threshold and realize multiroom navigation. Firstly, in a short time, robot runs a suitable distance when it is covered by navigator’s ultrasonic sensor. We can obtain a triangle with twice the distance between navigator and robot and the distance of robot motion. The forward angle of the robot can be conveniently obtained by the trigonometric functions. Comparing the robot’s current angle with expected angle, the robot could correct itself and realize the indoor linear navigation. Secondly, discovering dozens of the magnitude gaps between the distance of robot run and the distance between navigator and robot, we propose an optimized method using approximate scaling which increases efficiency by nearly 70.8%. Finally, to realize multiroom navigation, we introduce the conception of the depth-first search stack and a unique encode rule on rooms and navigators. It is proved by extensive quantitative evaluations that the proposed method realizes indoor full coverage at a lower cost than other state-of-the-art indoor vision navigation schemes, such as ORB-SLAM.
机译:由于需要之字形重叠策略,因此长期线性运动对于扫地机器人至关重要。但是,现有的室内扫地机器人导航算法存在很多问题。例如,具有高复杂度的算法需要较高的硬件性能,并且无法在夜间工作。为了克服这些问题,本文提出了一种新的室内机器人通过导航器直线运动的方法(SLMN),以确保机器人的长线性运动并具有可接受的误差阈值并实现多房间导航。首先,在很短的时间内,当导航器的超声波传感器覆盖机器人时,机器人会运行适当的距离。我们可以得到一个三角形,其导航器和机器人之间的距离是机器人运动距离的两倍。机器人的前角可以通过三角函数方便地获得。将机器人的当前角度与预期角度进行比较,机器人可以自行校正并实现室内线性导航。其次,通过发现机器人运行距离与导航器与机器人之间距离的数十个量级差距,我们提出了一种使用近似缩放的优化方法,该方法可将效率提高近70.8%。最后,为了实现多房间导航,我们介绍了深度优先搜索堆栈的概念以及针对房间和导航器的唯一编码规则。通过大量的定量评估证明,与其他最先进的室内视觉导航方案(例如ORB-SLAM)相比,该方法以较低的成本实现了室内全覆盖。

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