Open Journal Systems

Towards Industry 4.0: Color-based object sorting using a robot arm and real-time object detection

Ze Chern Ong, Kok Hoe Ho, Wen-Shyan Chua

Abstract

With the introduction of Industry 4.0, automation and robotics have made great strides, enabling enterprises to improve their manufacturing processes for increased productivity and efficiency. This project introduces a novel method for implementing Industry 4.0 concepts through color-based object sorting employing a robot arm with real-time object identification capabilities. Creating a reliable and effective system that can automatically categorize items based on their color properties is the main goal of this project. To enable seamless object recognition and manipulation in real time, the suggested system integrates robotic manipulation with computer vision algorithms. The system makes use of a convolutional neural network (CNN) for precise object detection, using recent advancements in deep learning and image processing, allowing the robot arm to interact with a variety of items effectively. The training phase and the sorting phase are the two key phases of the approach. The CNN model is trained on a sizable dataset of labeled objects during the training phase to recognize various colors and forms. In order for the robotic arm to recognize things as they go along the conveyor belt and sort them into predetermined bins according to their respective colors, the trained model must be integrated with the robotic arm during the sorting phase. Several experiments are carried out with various lighting setups and object arrangements to evaluate the performance of the suggested system. The outcomes show how well the system performs in terms of exact object detection and reliable sorting. The system’s capacity to effectively handle a variety of objects and adapt to changing environmental conditions further emphasizes its suitability for use in actual industrial scenarios. This project has important ramifications for the manufacturing sector, enabling improved automation capabilities and cost-efficiency. An important step towards implementing Industry 4.0 principles is the seamless integration of color-based object sorting and real-time object detection using a robotic arm. This will allow industries to optimize their production processes, minimize human intervention, and increase overall productivity. Further developments in robotics and computer vision are anticipated to push the limits of automation and open the door for more advanced and intelligent industrial systems as technology develops.


Keywords

Industry 4.0; color-based sorting; robotic arm; real-time object detection; convolutional neural network (CNN); Internet of things (IoT)

Full Text:

PDF

References

1. Harris T, Pollette C. How robots work. Available online: https://science.howstuffworks.com/robot2.htm#:~:text=The%20computer%20controls%20the%20robot,same%20movement%20over%20and%20over (accessed on 10 November 2023).

2. Palmai K, Smale W. The robot chefs that can cook your Christmas dinner. Available online: https://www.bbc.com/news/business-59651334 (accessed on 10 November 2023).

3. Fairchild M. Top industries using robots. Available online: https://www.howtorobot.com/expert-insight/top-industries-using-robots (accessed on 10 November 2023).

4. Fu S, Bhavsar PC. Robotic arm control based on Internet of things. In: Proceedings of the 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT); 3 May 2019; Farmingdale, NY, USA. pp. 1–6.

5. IOT based robotic arm project using NodeMCU. Available online: https://iotdesignpro.com/projects/iot-based-robotic-arm-using-esp8266 (accessed on 10 November 2023).

6. Available online: https://www.iaasiaonline.com/controlling-robotic-arms-2/ (accessed on 10 November 2023).

7. Lin HI, Lin YH. A novel teaching system for industrial robots. Sensors 2014; 14(4): 6012–6031. doi: 10.3390/s140406012

8. Software pendant. Available online: https://www.yaskawa.eu.com/products/software/productdetail/product/software-pendant_1673 (accessed on 10 November 2023).

9. Jan Y, Hassan S, Sanghun P, Jungwon Y. Smartphone based control architecture of teaching pendant for Industrial manipulators. In: Proceedings of the 2013 4th International Conference on Intelligent Systems, Modelling and Simulation; 29–31 January 2013; Bangkok, Thailand. pp. 370–375.

10. Sethi P, Sarangi SR. Internet of things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering 2017; 2017: 1–25. doi: 10.1155/2017/9324035

11. Li XQ, Ding X, Zhang Y, et al. IoT family robot based on raspberry Pi. In: Proceedings of the 2016 International Conference on Information System and Artificial Intelligence (ISAI); 24–26 June 2016; Hong Kong, China. pp. 622–625.

12. Jain RK, Saikia BJ, Rai NP, Ray PP. Development of web-based application for mobile robot using IOT platform. In: Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 1–3 July 2020; Kharagpur, India. pp. 1–6.

13. Qadri I, Muneer A, Fati SM. Automatic robotic scanning and inspection mechanism for mines using IoT. IOP Conference Series: Materials Science and Engineering 2021; 1045(1): 012001. doi: 10.1088/1757-899x/1045/1/012001

14. How to make robot arm Nodemcu Esp8266 (access point) IOT project. Available online: https://youtu.be/r7712qCkngs (accessed on 10 November 2023).

15. IoT based robotic arm using NodeMCU ESP8266. Available online: https://youtu.be/ZG2Ek-Z5i1Q (accessed on 10 November 2023).

16. How to mechatronics. DIY Arduino robot arm with smartphone control. Available online: https://youtu.be/_B3gWd3A_SI (accessed on 10 November 2023).

17. Robot arm using ESP32 and smartphone | Complete robot arm assembly. Available online: (https://youtu.be/cVSvg6VQhGU (accessed on 10 November 2023).

18. Getting started with the ESP32. Available online: https://dronebotworkshop.com/esp32-intro/ (accessed on 10 November 2023).

19. Shatwell DG, Murray V, Barton A. Real-time ore sorting using color and texture analysis. International Journal of Mining Science and Technology 2023; 33(6): 659–674. doi: 10.1016/j.ijmst.2023.03.004

20. We make affordable robots for all. Available online: https://store-ufactory-cc.myshopify.com/products/xarm-camera-module-2020 (accessed on 10 November 2023).

21. Nelson J. How to label images for computer vision models. Available online: https://blog.roboflow.com/tips-for-how-to-label-images/ (accessed on 10 November 2023).

22. TensorFlow 2 detection model zoo Available online: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md (accessed on 10 November 2023).

23. T. H. Team, “Try hivemq,” MQTT Quality of Service (QoS) 0,1, & 2 – MQTT Essentials: Part 6. Available online: https://www.hivemq.com/blog/mqtt-essentials-part-6-mqtt-quality-of-service-levels/ (accessed on 18 November 2023).


(46 Abstract Views, 15 PDF Downloads)

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Ze Chern Ong, Kok Hoe Ho, Wen-Shyan Chua

License URL: http://creativecommons.org/licenses/by/4.0/


This site is licensed under a Creative Commons Attribution 4.0 International License.