Research on optimal path planning technology for industrial robots based on improved IDE algorithm

[1] Arents, J., & Greitans, M. (2022). Smart industrial robot control trends, challenges and opportunities within manufacturing. Applied Sciences, 12(2), 937. Search in Google Scholar

[2] Liu, Z., Liu, Q., Xu, W., Wang, L., & Zhou, Z. (2022). Robot learning towards smart robotic manufacturing: A review. Robotics and Computer-Integrated Manufacturing, 77, 102360. Search in Google Scholar

[3] Evjemo, L. D., Gjerstad, T., Grøtli, E. I., & Sziebig, G. (2020). Trends in smart manufacturing: Role of humans and industrial robots in smart factories. Current Robotics Reports, 1, 35-41. Search in Google Scholar

[4] Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics, 1, 58-75. Search in Google Scholar

[5] Buerkle, A., Eaton, W., Al-Yacoub, A., Zimmer, M., Kinnell, P., Henshaw, M., ... & Lohse, N. (2023). Towards industrial robots as a service (IRaaS): Flexibility, usability, safety and business models. Robotics and Computer-Integrated Manufacturing, 81, 102484. Search in Google Scholar

[6] Perzylo, A., Rickert, M., Kahl, B., Somani, N., Lehmann, C., Kuss, A., ... & Danzer, M. (2019). SMErobotics: Smart robots for flexible manufacturing. IEEE Robotics & Automation Magazine, 26(1), 78-90. Search in Google Scholar

[7] Bilancia, P., Schmidt, J., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2023). An overview of industrial robots control and programming approaches. Applied Sciences, 13(4), 2582. Search in Google Scholar

[8] Hongshuai, Y. (2021). Research on the Application of Industrial Robots in Automation Control. Curriculum and Teaching Methodology, 4(4), 132-138. Search in Google Scholar

[9] Zhou, L., Wang, F., Wang, N., & Yuan, T. (2021, August). Application of industrial robots in automated production lines under the background of intelligent manufacturing. In Journal of Physics: Conference Series (Vol. 1992, No. 4, p. 042050). IOP Publishing. Search in Google Scholar

[10] Klimchik, A., Ambiehl, A., Garnier, S., Furet, B., & Pashkevich, A. (2017). Efficiency evaluation of robots in machining applications using industrial performance measure. Robotics and Computer-Integrated Manufacturing, 48, 12-29. Search in Google Scholar

[11] Huan-Kun, H. S. U., Hsiang-Yuan, T. I. N. G., Huang, M. B., & Huang, H. P. (2021). Intelligent fault detection, diagnosis and health evaluation for industrial robots. Mechanics, 27(1), 70-79. Search in Google Scholar

[12] Qin, B., Luo, Q., Luo, Y., Zhang, J., Liu, J., & Cui, L. (2020, June). Research and application of key technologies of edge computing for industrial robots. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (Vol. 1, pp. 2157-2164). IEEE. Search in Google Scholar

[13] Mesmer, P., Neubauer, M., Lechler, A., & Verl, A. (2022). Robust design of independent joint control of industrial robots with secondary encoders. Robotics and Computer-Integrated Manufacturing, 73, 102232. Search in Google Scholar

[14] Ratiu, M., & Prichici, M. A. (2017). Industrial robot trajectory optimization-a review. In MATEC web of conferences (Vol. 126, p. 02005). EDP Sciences. Search in Google Scholar

[15] Wang, G., & Arora, H. (2021). Research on continuous trajectory planning of industrial welding robot based on cad technology. Computer-Aided Design and Applications, 19(2), 74-87. Search in Google Scholar

[16] Luo, X., Li, S., Liu, S., & Liu, G. (2019). An optimal trajectory planning method for path tracking of industrial robots. Robotica, 37(3), 502-520. Search in Google Scholar

[17] Huang, J., Hu, P., Wu, K., & Zeng, M. (2018). Optimal time-jerk trajectory planning for industrial robots. Mechanism and Machine Theory, 121, 530-544. Search in Google Scholar

[18] Kim, J., & Croft, E. A. (2019). Online near time-optimal trajectory planning for industrial robots. Robotics and Computer-Integrated Manufacturing, 58, 158-171. Search in Google Scholar

[19] Sathiya, V., & Chinnadurai, M. (2019). Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning. Robotica, 37(8), 1363-1382. Search in Google Scholar

[20] Larsen, L., & Kim, J. (2021). Path planning of cooperating industrial robots using evolutionary algorithms. Robotics and Computer-Integrated Manufacturing, 67, 102053. Search in Google Scholar

[21] Das, S. D., Bain, V., & Rakshit, P. (2018, June). Energy optimized robot arm path planning using differential evolution in dynamic environment. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1267-1272). IEEE. Search in Google Scholar

[22] Wang, F., Wu, Z., & Bao, T. (2022). Time-jerk optimal trajectory planning of industrial robots based on a hybrid WOA-GA algorithm. Processes, 10(5), 1014. Search in Google Scholar

[23] Zhuozhen Tang, Bin Xue, Hongzhong Ma & AhmadRad. (2024). Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots. Journal of Field Robotics(5),1426-1437. Search in Google Scholar

[24] Mohit Ranjan Panda,Pradipta Das & Saroj Pradhan. (2017). Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment. Journal of King Saud University - Computer and Information Sciences Search in Google Scholar

[25] Kazim Issraa Jwad, Tan Yuegang & Qaseer Layth. (2021). Integration of DE Algorithm with PDC-APF for Enhancement of Contour Path Planning of a Universal Robot. Applied Sciences(14),6532-6532. Search in Google Scholar

[26] Lv Zheng, Qiang Fangfang & zhan Yu. (2022). Node positioning based on IPSO-IDE in WSNs. Evolutionary Intelligence(1),483-492. Search in Google Scholar