In this project, we propose a human-tracking robot that can be used in commercial establishments such as airports and factories. The human-tracking process involves three main steps. The first is robust person identification using QR code recognition, and is the most important step in human tracking. The second is location detection using shape-based pattern matching in order to determine the position of the QR code when the person being tracked moves far from the robot. Finally, the third step is robot control to maintain an appropriate distance for human tracking. In experiments measuring QR code recognition rates, it was shown that QR code recognition was 99.9% and that location detection is robust. In a robot-control experiment, tracking was shown to be accurate, and during tracking, the robot maintained an appropriate distance from the human. This robot equipped with a raspberrypi3 processor which controls the whole system. Ultrasonic sensor, Pi camera, DC motor along with driver and LCD is interfaced to the raspberrypi3 processor. When the pi camera detects the QR CODE this data process to the raspberry pi and decoded by the raspberry pi using image processing technology. Then raspberry pi controls the robot towards the human, so this robot follows the human with particular distance using DC motors.  Ultrasonic sensor will measure the distance of the object(person) and will be display on LCD module. If the person comes closer to the robot than the maximum distance, the robot will stop automatically. To achieve this task raspberry pi loaded program written in python language.

   

The objectives of the project are:

1. (a) Continuous identification of the individual person being followed.
2. (b) Detection of the location of the person being followed.
3. (c) Control of the robot based on the detected location data.
4. As such, we have proposed a human-following robot that uses a QR-code tag for tracking, considering that it would be difficult to satisfy condition
5. Using only image processing to implement following behavior.
6. To achieve this task using Raspberry Pi3 processor.

  The project provides learning’s on the following advancements:

1. Learn about Image processing Technology.
2. Raspberry pi working and programming.
3. Pi camera interfacing to the raspberry pi.

The major building blocks of this project are:

• Power supply.
• Raspberry Pi3A+ Processor.
• SD Card.
• Ultrasonic sensor.
• Pi camera.
• LM2596 voltage regulator.
• Rechargeable Battey.
• LED indicators.
• DC motors with driver.
• LCD display.

  Software’s used:

• Python language.
• OpenCV image processing.
• Linux OS.

   

   

Block Diagram:  

   

 

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