Autonomous tour guide robot by using ultrasonic range sensors and QR code recognition in indoor environment

Abstract

This paper addresses the challenge of mobile robot navigation in indoor environments. There is a critical need for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Currently, researchers are exploring various approaches for this problem. The one we are presenting in this paper is based on QR (Quick Response) codes to provide location references for mobile robots. The mobile robot is equipped with a Smartphone that is programmed to detect and read information on QR codes that are strategically placed in the operating environment of the robot. The mobile robot can perform the autonomous run throughout the guide route by using real-time QR code recognition. The lab information on QR code is played to the visitors using Text-to-Speech provided through Android device. Ultrasonic range sensors which can detect objects and measure distances with high accuracy are used to implement the wallfollowing and obstacle-avoidance behaviors. The collected sonar range information by ultrasonic range sensors is processed by a microcontroller that autonomously controls a tour guide robot. An algorithm based on a proportional-integral-derivative (PID) control is applied to the tour guide robot to perform more accurate robot motion control. A Bluetooth technology is used to send stored information on QR codes from the Smartphone to the tour guide robot wirelessly. The experimental setup of the tour guide robot along with the successful implementation of the efficient method for a navigation technique is presented.

QR-code based Localization for Indoor Mobile Robot with validation using a 3D optical tracking instrument

“…The app contains a main control program and task to handle the QR-code recognition, circle shape detection, reading of the Smartphone's orientation sensor, and access to a database. The detailed implementation of Smartphone app for a tour guide robot task has been fully explained in our previous paper [4]. The previous app has now been extended to provide information about the sensor value accessed from the orientation sensor of the Smartphone.…”

“…Real_height =Dist_real * sin(θ) (4) where Real_base and Real_height are defined as x-axis and y-axis distance, respectively between the position of the robot and the center of the QR-code in the real-world domain. The center coordinates of the QR-code in image frame of the Smartphone camera play an important role to estimate the actual x and y coordinates of the robot position.…”

Mobile Robot Transportation for Multiple Labware with Hybrid Pose Correction in Life Science Laboratories

“…RFID reader and IC tag methods are robust but unsuitable for large environments due to the expensive installation of IC tags [28]. Image vision methods give the robot accurate information about its environment [29] but fail to work properly in low light levels with certain complex situations. In addition, the required time is not satisfying.…”

Temporal segmentation of environmental map based on changing rate of activity areas and improvement of tracking method using LRF

“…Significant research on guidance by autonomous mobile robots has been conducted [6][7]. Although the previously investigated robots can guide individuals who are selected beforehand, they cannot detect people who are in need of such services.…”