Indoor localization and navigation for a mobile robot equipped with rotating ultrasonic sensors using a smartphone as the robot's brain

Jongil Lim, S. Lee, G. Tewolde, Jaerock Kwon
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引用次数: 31

Abstract

Identifying the exact current location of a robot is a fundamental prerequisite for successful robot navigation. To precisely localize a robot, one popular way is to use particle filters that estimate the posterior probabilistic density of a robot's state space. But this Bayesian recursion approach is computationally expensive. Most microcontrollers in a small mobile robot cannot afford it. We propose to use a smartphone as a robot's brain in which heavy-duty computations take place whereas an embedded microcontroller on the robot processes rudimentary sensors such as ultrasonic and touch sensors. In our design, a smarphone is wirelessly connected to a robot via Bluetooth by which distance measurements from the robot are sent to the smartphone. Then the smartphone takes responsible for computationally expensive operations like executing the particle filter algorithm. Also we propose to use rotating ultrasonic sensors to reduce the number of sensors needed as well as time of distant measurements. In this paper, we designed a mobile robot and its control architecture to demonstrate that the robot can navigate indoor environment while avoiding obstacles and localize its current position. Several experiments were conducted to show feasibility of the rotating sensors and a smartphone brain for a mobile robot.
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装有旋转超声波传感器的移动机器人的室内定位和导航,使用智能手机作为机器人的大脑
识别机器人当前的准确位置是机器人成功导航的基本前提。为了精确定位机器人,一种流行的方法是使用粒子滤波来估计机器人状态空间的后验概率密度。但是这种贝叶斯递归方法在计算上是昂贵的。大多数小型移动机器人的微控制器都负担不起。我们建议使用智能手机作为机器人的大脑,其中进行繁重的计算,而机器人上的嵌入式微控制器处理基本的传感器,如超声波和触摸传感器。在我们的设计中,智能手机通过蓝牙与机器人无线连接,机器人的距离测量数据通过蓝牙发送到智能手机。然后,智能手机负责执行粒子过滤算法等计算成本高昂的操作。我们还建议使用旋转超声传感器,以减少所需的传感器数量和远程测量的时间。在本文中,我们设计了一个移动机器人及其控制体系结构,以证明机器人可以在室内环境中导航,同时避开障碍物并定位其当前位置。为了证明旋转传感器和智能手机大脑用于移动机器人的可行性,研究人员进行了几项实验。
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