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Year 2023, , 799 - 815, 28.12.2023

Ahmet Yüksek , Ahmet Utku Elik

https://doi.org/10.17776/csj.1366104

Abstract

References

  • [1] Fard N. E., Selmi R. R., Khorasani K., Public Policy Challenges, Regulations, Oversight, Technical, and Ethical Considerations for Autonomous Systems: A Survey, IEEE Technol. Soc. Mag., 42 (1) (2023) 45-53.
  • [2] Pratihar D. K., Jain L. C., Ed.,. Studies in computational intelligence, Intelligent autonomous systems: foundations and applications. Berlin: Springer Verlag, 275 (2010).
  • [3] Li J., Cheng H., Guo H., Qiu S., Survey on Artificial Intelligence for Vehicles, Automot. Innov., 1 (1) (2018) 2-14
  • [4] Veres S. M., Molnar L., Lincoln N. K., Morice C. P., Autonomous vehicle control systems — a review of decision making, Proc. Inst. Mech. Eng. Part J. Syst. Control Eng., 225 (2) (2011) 155-195.
  • [5] Ma Y., Wang Z., Yang H., Yang L., Artificial intelligence applications in the development of autonomous vehicles: a survey, IEEECAA J. Autom. Sin., 7 (2020) 315-329.
  • [6] Reis W. P. N. D., Couto G. E., Junior O. M., Automated guided vehicles position control: a systematic literature review, J. Intell. Manuf., 34 (4) ( 2023) 1483-1545.
  • [7] Ryck M. D., Versteyhe M., Debrouwere F., Automated guided vehicle systems, state-of-the-art control algorithms and techniques, J. Manuf. Syst., 54 (2020) 152-173.
  • [8] Grand View Research, GVR Report cover Automated Guided Vehicle Market Size, Share & Trends Report Automated Guided Vehicle Market Size, Share & Trends Analysis Report By Vehicle Type, By Navigation Technology, By Application, By End-Use Industry, By Component, By Battery Type, By Region, And Segment Forecasts, 2023 - 2030. Available at : https://www.grandviewresearch.com/industry-analysis/automated-guided-vehicle-agv-market, Retrieved 2023.
  • [9] Wan J., Tang S., Hua Q., Li D., Liu C., Lloret J., Context-Aware Cloud Robotics for Material Handling in Cognitive Industrial Internet of Things, IEEE Internet Things J., 5 (4) (2018). 2272-2281.
  • [10] Ismail A. H., Ramli H. R., Ahmad M. H., Marhaban M. H., Vision-based system for line following mobile robot, 2009 IEEE Symposium on Industrial Electronics & Applications, Kuala Lumpur, Malaysia: IEEE, (2009) 642-645.
  • [11] A. VehicleManufacturers, I. Savant Automation, A. Motion, A. Inc., J. Corporation, A. Eckhart and I. Ward Systems, AGV Manufacturers | AGV Suppliers. Available at : https://www.automaticguidedvehicles.com/ ,Retrieved: 2023.
  • [12] Fedorko G., Honus S., Salai R., Comparison of the Traditional and Autonomous AGV Systems, MATEC Web Conf., 134 (2017) 13.
  • [13] Ilas C., Electronic sensing technologies for autonomous ground vehicles: A review, 2013 8TH Internatıonal Symposıum On Advanced Topıcs In Electrıcal Engıneerıng (Atee), Bucharest, Romania: IEEE, (2013) 1-6.
  • [14] Bostelman R., Hong T., Cheok G., Navigation performance evaluation for automatic guided vehicles, 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA: IEEE, (2015) 1-6.
  • [15] Lynch L., Newe T., Clifford J., Coleman J., Walsh J., Toal D., Automated Ground Vehicle (AGV) and Sensor Technologies- A Review, 2018 12th International Conference on Sensing Technology (ICST), Limerick: IEEE, (2018) 347-352.
  • [16] Ishikawa S., Kuwamoto H., Ozawa S., Visual navigation of an autonomous vehicle using white line recognition, IEEE Trans. Pattern Anal. Mach. Intell., 10 (5) (1988) 743-749.
  • [17] Shah M., Rawal V., Dalwadi J., Design Implementation of High-Performance Line Following Robot, 2017 International Conference on Transforming Engineering Education (ICTEE), Pune: IEEE, (2017) 1-5.
  • [18] Thanh V. N., Vinh D. P., Nghi N. T., Nam L. H., Toan D. L. H., Restaurant Serving Robot with Double Line Sensors Following Approach, 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China: IEEE, (2019) 235-239.
  • [19] Payne S. C., Awad E. M., The systems analyst as a knowledge engineer: can the transition be successfully made?, Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems - SIGBDP ’90, Orlando, Florida, United States: ACM Press, (1990) 155-169.
  • [20] La Salle A. J., Medsker L. R., The expert system life cycle: what have we learned from software engineering, Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems - SIGBDP ’90, Orlando, Florida, United States: ACM Press, (1990) 17-26.
  • [21] Zadeh L. A., Soft computing and fuzzy logic, IEEE Softw., 11 (6) (1994) 48-56.
  • [22] Kovasznay L. G., Joseph H., Image Processing, Proc. IRE, 43 (5) (1955) 560-570.
  • [23]Goguen J. A., Zadeh L. A., Fuzzy sets. Information and control, 8 (1965) 338–353. - Zadeh L. A., Similarity relations and fuzzy orderings. Information sciences, vol. 3 (1971) 177–200., J. Symb. Log., 38 (4) (1973) 656-657.
  • [24]Chen G., Pham T. T., Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. Boca Raton, FL: CRC Press, (2001).
  • [25]Jang J.S. R., ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern., 23 (3) (1993) 665-685.
  • [26]Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief], IEEE Trans. Neural Netw., 8 (5) (1997) 1219-1219.
  • [27] Cybenko G., Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst., 2 (4) (1989) 303-314.
  • [28]Multilayer feedforward networks are universal approximators - ScienceDirect. Avaliable at : https://www.sciencedirect.com/science/article/pii/0893608089900208 , Retrieved: (2018)
  • [29]Jang J. S. R., Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, CUMINCAD, (1997). Avaliable at : http://papers.cumincad.org/cgi-bin/works/Show?d036 , Retrieved: (2018)
  • [30]Yuksek A. G., Hava Kirliliği Tahmininde Çoklu Regresyon Analizi Ve Yapay Sinir Ağları Yönteminin Karşılaştırılması, Doktora Tez, Cumhuriyet Üniversitesi, Sivas, (2007). Avaliable at: https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp. Retrieved : (2018).
  • [31] Wu X., Li W., Hong D., Tao R., Du Q., Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey, IEEE Geosci. Remote Sens. Mag., 10 (1) (2022) 91-124.
  • [32]Sahba R., Sahba A., Sahba F., Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles, 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada: IEEE, (2020) 0427-0431.
  • [33]Pawar P. G., Devendran V., Scene Understanding: A Survey to See the World at a Single Glance, 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India: IEEE, (2019) 182-186.
  • [34]Miles V., Gurr F., Giani S., Camera-Based System for the Automatic Detection of Vehicle Axle Count and Speed Using Convolutional Neural Networks, Int. J. Intell. Transp. Syst. Res., 20 (3) (2022) 778-792.
  • [35]Sarwade J., Shetty S., Bhavsar A., Mergu M., Talekar A., Line Following Robot Using Image Processing, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India: IEEE, (2019) 1174-1179.
  • [36]Hu Z., I2C Protocol Design for Reusability, 2010 Third International Symposium on Information Processing, Qingdao, Shandong, China: IEEE, (2010) 83-86.
  • [37] Hudec M., Fuzzy Set and Fuzzy Logic Theory in Brief, Fuzziness in Information Systems, Cham: Springer International Publishing, (2016) 1-32.
  • [38]Dubois D., Prade H., Ed., Fundamentals of Fuzzy Sets, The Handbooks of Fuzzy Sets Series, Boston, MA: Springer US, (2000).
  • [39]Talpur N., Salleh M. N. M., Hussain K., An investigation of membership functions on performance of ANFIS for solving classification problems, IOP Conf. Ser. Mater. Sci. Eng., (2017) 226.
  • [40]González-Sopeña J. M., Pakrashi V., Ghosh B., An overview of performance evaluation metrics for short-term statistical wind power forecasting, Renew. Sustain. Energy Rev., 138 (2021) 515.

Development of Image Processing Based Line Tracking Systems for Automated Guided Vehicles with ANFIS and Fuzzy Logic

Year 2023, , 799 - 815, 28.12.2023

Ahmet Yüksek , Ahmet Utku Elik

https://doi.org/10.17776/csj.1366104

Abstract

Automated Guided Vehicles (AGVs) are robotic vehicles with the ability to move using mapping and navigation technologies to perform tasks assigned to them, guided by guides. Using sensor data such as laser scanners, cameras, magnetic stripes or colored stripes, they can sense their environment and move safely according to defined routes. The basic requirement of motion planning is to follow the path and route with minimum error even under different environmental factors. The key factor here is the most successful detection of the guiding structure of a system moving on its route. The proposed system is to equip a mechanical system that can produce very fast outputs and autonomous motion as a result of combining different algorithms with different hardware structures. In the line detection process, the wide perspective image from the camera is designed to be gradually reduced and converted into image information that is more concise but representative of the problem in a narrower perspective. In this way, the desired data can be extracted with faster processing over less information. In this study, the image information is divided into two parts and planned as two different sensors. The fact that the line information was taken from two different regions of the image at a certain distance enabled the detection of not only the presence of the line but also the flow direction. With the fuzzy system, the performance of the system was increased by generating PWM values on two different hardware structures, loading image capture, image processing processes and driving the motors. In order to determine the membership function parameters of the fuzzy system for each input, the ANFIS approach was used on the data set modeling the system. The outputs produced by the ANFIS model were combined into a single fuzzy system with two outputs from the system rules framework and the system was completed. The success of the algorithms was ensured by partitioning the task distribution in the hardware structure. With its structure and success in adapting different technologies together, a system that can be recommended for similar problems has been developed.

Keywords

Automated Guided Vehicles Line Following Image Processing Fuzzy Logic Systems ANFIS

References

  • [1] Fard N. E., Selmi R. R., Khorasani K., Public Policy Challenges, Regulations, Oversight, Technical, and Ethical Considerations for Autonomous Systems: A Survey, IEEE Technol. Soc. Mag., 42 (1) (2023) 45-53.
  • [2] Pratihar D. K., Jain L. C., Ed.,. Studies in computational intelligence, Intelligent autonomous systems: foundations and applications. Berlin: Springer Verlag, 275 (2010).
  • [3] Li J., Cheng H., Guo H., Qiu S., Survey on Artificial Intelligence for Vehicles, Automot. Innov., 1 (1) (2018) 2-14
  • [4] Veres S. M., Molnar L., Lincoln N. K., Morice C. P., Autonomous vehicle control systems — a review of decision making, Proc. Inst. Mech. Eng. Part J. Syst. Control Eng., 225 (2) (2011) 155-195.
  • [5] Ma Y., Wang Z., Yang H., Yang L., Artificial intelligence applications in the development of autonomous vehicles: a survey, IEEECAA J. Autom. Sin., 7 (2020) 315-329.
  • [6] Reis W. P. N. D., Couto G. E., Junior O. M., Automated guided vehicles position control: a systematic literature review, J. Intell. Manuf., 34 (4) ( 2023) 1483-1545.
  • [7] Ryck M. D., Versteyhe M., Debrouwere F., Automated guided vehicle systems, state-of-the-art control algorithms and techniques, J. Manuf. Syst., 54 (2020) 152-173.
  • [8] Grand View Research, GVR Report cover Automated Guided Vehicle Market Size, Share & Trends Report Automated Guided Vehicle Market Size, Share & Trends Analysis Report By Vehicle Type, By Navigation Technology, By Application, By End-Use Industry, By Component, By Battery Type, By Region, And Segment Forecasts, 2023 - 2030. Available at : https://www.grandviewresearch.com/industry-analysis/automated-guided-vehicle-agv-market, Retrieved 2023.
  • [9] Wan J., Tang S., Hua Q., Li D., Liu C., Lloret J., Context-Aware Cloud Robotics for Material Handling in Cognitive Industrial Internet of Things, IEEE Internet Things J., 5 (4) (2018). 2272-2281.
  • [10] Ismail A. H., Ramli H. R., Ahmad M. H., Marhaban M. H., Vision-based system for line following mobile robot, 2009 IEEE Symposium on Industrial Electronics & Applications, Kuala Lumpur, Malaysia: IEEE, (2009) 642-645.
  • [11] A. VehicleManufacturers, I. Savant Automation, A. Motion, A. Inc., J. Corporation, A. Eckhart and I. Ward Systems, AGV Manufacturers | AGV Suppliers. Available at : https://www.automaticguidedvehicles.com/ ,Retrieved: 2023.
  • [12] Fedorko G., Honus S., Salai R., Comparison of the Traditional and Autonomous AGV Systems, MATEC Web Conf., 134 (2017) 13.
  • [13] Ilas C., Electronic sensing technologies for autonomous ground vehicles: A review, 2013 8TH Internatıonal Symposıum On Advanced Topıcs In Electrıcal Engıneerıng (Atee), Bucharest, Romania: IEEE, (2013) 1-6.
  • [14] Bostelman R., Hong T., Cheok G., Navigation performance evaluation for automatic guided vehicles, 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA: IEEE, (2015) 1-6.
  • [15] Lynch L., Newe T., Clifford J., Coleman J., Walsh J., Toal D., Automated Ground Vehicle (AGV) and Sensor Technologies- A Review, 2018 12th International Conference on Sensing Technology (ICST), Limerick: IEEE, (2018) 347-352.
  • [16] Ishikawa S., Kuwamoto H., Ozawa S., Visual navigation of an autonomous vehicle using white line recognition, IEEE Trans. Pattern Anal. Mach. Intell., 10 (5) (1988) 743-749.
  • [17] Shah M., Rawal V., Dalwadi J., Design Implementation of High-Performance Line Following Robot, 2017 International Conference on Transforming Engineering Education (ICTEE), Pune: IEEE, (2017) 1-5.
  • [18] Thanh V. N., Vinh D. P., Nghi N. T., Nam L. H., Toan D. L. H., Restaurant Serving Robot with Double Line Sensors Following Approach, 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China: IEEE, (2019) 235-239.
  • [19] Payne S. C., Awad E. M., The systems analyst as a knowledge engineer: can the transition be successfully made?, Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems - SIGBDP ’90, Orlando, Florida, United States: ACM Press, (1990) 155-169.
  • [20] La Salle A. J., Medsker L. R., The expert system life cycle: what have we learned from software engineering, Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems - SIGBDP ’90, Orlando, Florida, United States: ACM Press, (1990) 17-26.
  • [21] Zadeh L. A., Soft computing and fuzzy logic, IEEE Softw., 11 (6) (1994) 48-56.
  • [22] Kovasznay L. G., Joseph H., Image Processing, Proc. IRE, 43 (5) (1955) 560-570.
  • [23]Goguen J. A., Zadeh L. A., Fuzzy sets. Information and control, 8 (1965) 338–353. - Zadeh L. A., Similarity relations and fuzzy orderings. Information sciences, vol. 3 (1971) 177–200., J. Symb. Log., 38 (4) (1973) 656-657.
  • [24]Chen G., Pham T. T., Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. Boca Raton, FL: CRC Press, (2001).
  • [25]Jang J.S. R., ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern., 23 (3) (1993) 665-685.
  • [26]Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief], IEEE Trans. Neural Netw., 8 (5) (1997) 1219-1219.
  • [27] Cybenko G., Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst., 2 (4) (1989) 303-314.
  • [28]Multilayer feedforward networks are universal approximators - ScienceDirect. Avaliable at : https://www.sciencedirect.com/science/article/pii/0893608089900208 , Retrieved: (2018)
  • [29]Jang J. S. R., Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, CUMINCAD, (1997). Avaliable at : http://papers.cumincad.org/cgi-bin/works/Show?d036 , Retrieved: (2018)
  • [30]Yuksek A. G., Hava Kirliliği Tahmininde Çoklu Regresyon Analizi Ve Yapay Sinir Ağları Yönteminin Karşılaştırılması, Doktora Tez, Cumhuriyet Üniversitesi, Sivas, (2007). Avaliable at: https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp. Retrieved : (2018).
  • [31] Wu X., Li W., Hong D., Tao R., Du Q., Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey, IEEE Geosci. Remote Sens. Mag., 10 (1) (2022) 91-124.
  • [32]Sahba R., Sahba A., Sahba F., Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles, 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada: IEEE, (2020) 0427-0431.
  • [33]Pawar P. G., Devendran V., Scene Understanding: A Survey to See the World at a Single Glance, 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India: IEEE, (2019) 182-186.
  • [34]Miles V., Gurr F., Giani S., Camera-Based System for the Automatic Detection of Vehicle Axle Count and Speed Using Convolutional Neural Networks, Int. J. Intell. Transp. Syst. Res., 20 (3) (2022) 778-792.
  • [35]Sarwade J., Shetty S., Bhavsar A., Mergu M., Talekar A., Line Following Robot Using Image Processing, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India: IEEE, (2019) 1174-1179.
  • [36]Hu Z., I2C Protocol Design for Reusability, 2010 Third International Symposium on Information Processing, Qingdao, Shandong, China: IEEE, (2010) 83-86.
  • [37] Hudec M., Fuzzy Set and Fuzzy Logic Theory in Brief, Fuzziness in Information Systems, Cham: Springer International Publishing, (2016) 1-32.
  • [38]Dubois D., Prade H., Ed., Fundamentals of Fuzzy Sets, The Handbooks of Fuzzy Sets Series, Boston, MA: Springer US, (2000).
  • [39]Talpur N., Salleh M. N. M., Hussain K., An investigation of membership functions on performance of ANFIS for solving classification problems, IOP Conf. Ser. Mater. Sci. Eng., (2017) 226.
  • [40]González-Sopeña J. M., Pakrashi V., Ghosh B., An overview of performance evaluation metrics for short-term statistical wind power forecasting, Renew. Sustain. Energy Rev., 138 (2021) 515.
There are 40 citations in total.

Details

Primary Language English
Subjects Statistics (Other)
Journal Section Natural Sciences
Authors

Ahmet Yüksek 0000-0001-7709-6360

Ahmet Utku Elik 0009-0009-0298-9944

Publication Date December 28, 2023
Submission Date September 25, 2023
Acceptance Date November 22, 2023
Published in Issue Year 2023

Cite

APA Yüksek, A., & Elik, A. U. (2023). Development of Image Processing Based Line Tracking Systems for Automated Guided Vehicles with ANFIS and Fuzzy Logic. Cumhuriyet Science Journal, 44(4), 799-815. https://doi.org/10.17776/csj.1366104