Abstract
The goal of Industry 4.0 is to achieve a higher level of operational efficiency and productivity, as well as a higher level of automatization. The automation is considered in the manufacturing industry to improve the efficiency of production processes. Also, the measurement at the nano-level on the surface of the target object by a machine has been considered for automation, but there are problems such as the need for high cost and a large amount of time for measurement. In this paper, we propose a robot vision system based on an intelligent algorithm for recognizing micro-roughness on arbitrary surfaces. The proposed system is inexpensive, make quick measurement and is capable of autonomously recognizing micro-roughness to improve the efficiency of production processes. The experimental results show that the Hill Climbing (HC) algorithm can reduce the movement vibration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dalenogare, L., et al.: The expected contribution of industry 4.0 : Int. J. Prod. Econ. (IJPE-2018) 204, 383-394 (2018)
Shang, L., et al.: Detection of rail surface defects based on CNN image recognition and classification. In: The IEEE 20th International Conference on Advanced Communication Technology (ICACT), pp. 45-51 (2018)
Li, J., et al.: Real-time detection of steel strip surface defects based on improved yolo detection network. IFAC-PapersOnLine 51(21), 76–81 (2018)
Oda, T., et al.: Design and implementation of a simulation system based on deep Q-network for mobile actor node control in wireless sensor and actor networks: In: Proceedings of The IEEE 31st International Conference on Advanced Information Networking and Applications Workshops, pp. 195-200 (2017)
Saito, N., Oda, T., Hirata, A., Hirota, Y., Hirota, M., Katayama, K.: Design and implementation of a DQN based AAV. In: Barolli, L., Takizawa, M., Enokido, T., Chen, H.-C., Matsuo, K. (eds.) BWCCA 2020. LNNS, vol. 159, pp. 321–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61108-8_32
Saito, N., Oda, T., Hirata, A., Toyoshima, K., Hirota, M., Barolli, L.: Simulation results of a DQN based AAV testbed in corner environment: a comparison study for normal DQN and TLS-DQN. In: Barolli, L., Yim, K., Chen, H.-C. (eds.) IMIS 2021. LNNS, vol. 279, pp. 156–167. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-79728-7_16
Saito, N., et al.: A Tabu list strategy based DQN for AAV mobility in indoor single-path environment: implementation and performance evaluation. Internet Things 14, 100394 (2021)
Saito, N., Oda, T., Hirata, A., Yukawa, C., Kulla, E., Barolli, L.: A LiDAR based mobile area decision method for TLS-DQN: improving control for AAV mobility. In: Barolli, L. (ed.) 3PGCIC 2021. LNNS, vol. 343, pp. 30–42. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-89899-1_4
Wang, H., et al.: Automatic illumination planning for robot vision inspection system. Neurocomputing 275, 19–28 (2018)
Zuxiang, W., et al.: Design of safety capacitors quality inspection robot based on machine vision. In: 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), pp. 1-4 (2017)
Li, J., et al.: Cognitive visual anomaly detection with constrained latent representations for industrial inspection robot. Appl. Soft Comput. 95, 106539 (2020)
Ruiz-del-Solar, J., et al.: A Survey on Deep Learning Methods for Robot Vision. arXiv preprint arXiv:1803.10862 (2018)
Matsui, T., et al.: FPGA implementation of a fuzzy inference based quadrotor attitude control system. In: Proceedings of IEEE GCCE-2021, pp. 691-692 (2021)
Saito, N., et al.: Approach of fuzzy theory and hill climbing based recommender for schedule of life. In: Proceedings of LifeTech-2020, pp. 368-369 (2020)
Ozera, K., et al.: A fuzzy approach for secure clustering in MANETs: effects of distance parameter on system performance. In: Proceedings of IEEE WAINA-2017, pp. 251-258 (2017)
Elmazi, D., et al.: Selection of secure actors in wireless sensor and actor networks using fuzzy logic. In: Proceedings of BWCCA-2015, pp. 125-131 (2015)
Elmazi, D., et al.: Selection of rendezvous point in content centric networks using fuzzy logic. In: Proceedings of NBiS-2015, pp. 345-350 (2015)
Zaeh, M.F., et al.: Improvement of the machining accuracy of milling robots. Prod. Eng. Res. Devel. 8(6), 737–744 (2014)
Yukawa, C., et al.: Design of a fuzzy inference based robot vision for CNN training image acquisition. In: Proceedings of IEEE GCCE-2020, pp. 871-872 (2021)
Liang, Q., et al.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)
Mendel, J.M.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14(6), 808–821 (2006)
Yosinski, J., et al.: How transferable are features in deep neural networks? arXiv preprint arXiv:1411.1792 (2014)
Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)
Yukawa, C., et al.: Design of a robot vision system for microconvex recognition. In: Barolli, L., Kulla, E., Ikeda, M. (eds) International Conference on Emerging Internetworking, Data and Web Technologies, Lecture Notes on Data Engineering and Communications Technologies, vol. 118, pp 366-374. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95903-6_39
Yukawa, C., et al.: An intelligent robot vision system for recognizing micro-roughness on arbitrary surfaces: experimental result for different methods. In: Barolli, L. (eds) International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, vol. 496, pp. 221-229. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08819-3_22
Yukawa, C., et al.: Evaluation of a fuzzy-based robotic vision system for recognizing micro-roughness on arbitrary surfaces: a comparison study for vibration reduction of robot arm. In: Barolli, L., Miwa, H., Enokido, T. (eds) International Conference on Network-Based Information Systems, vol. 526, pp 230-237 (2022). https://doi.org/10.1007/978-3-031-14314-4_23
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 20K19793.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yukawa, C. et al. (2023). Design of an Intelligent Robotic Vision System for Optimization of Robot Arm Movement. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2022. Lecture Notes in Networks and Systems, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-031-20029-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-20029-8_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20028-1
Online ISBN: 978-3-031-20029-8
eBook Packages: EngineeringEngineering (R0)