Hongkun Xiang1, Lihua Huang2, Tianyu Lan1, Jun Wang1, Yan Zhao1, Binlong He1
1School of Intelligence Technology, Geely University of China, Jianyang, Chengdu, Sichuan, 641400, China
2School of Electronic and Information Engineering, Geely University of China, Jianyang, Chengdu, 641400, China
We propose a Deep learning which is to be aimed at the method of robotic arm modeling algorithm. Firstly, the movement of the mechanical arm, raise the traditional intelligent grasping system, improve the SSD original model, and enhance the backbone network performance selectively, improve the depth of the applicability of deterministic strategy gradient (DDPG) algorithm for further research, to shorten the mechanical arm model debugging time to reach the goal of avoiding obstacles according to research on the mechanical arm dynamics modeling.This will make the robotic arm to have a high ability to adapt to the environment, and can provide researchers with ideas to solve the theoretical research and engineering implementation in this field after training and learning.
robotic arm; deeplearning; kinetic modeling; SSD algorithm; DDPG algorithm
Hongkun Xiang, Lihua Huang, Tianyu Lan, Jun Wang, Yan Zhao, Binlong He. Research on the robotic arm modeling algorithm based on deep learning. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 5: 1-7. https://doi.org/10.25236/IJFET.2023.050501.
 Craig J J. Introduction to robotics: mechanics and control[M]. India: Pearson Education, 2009.
 Shin S, Kang H J, Lim H K, et al. Robot calibration and modified command generation for the offline programming [C]. International Conference on Mechatronics Technology, 2007
 Lieto A, Bhatt M, Oltramari A, et al. The roleof cognitive architectures in general artificial intelligence [J]. Cognitive Systems Research, 2018, 48:1-3
 Yin Baocai, Wang Wentong, Wang Lichun. Review of deep learning studies [J]. Journal of Beijing University of Technology, 2015 (1).
 Du X D, Cai Y H, LuT, et al. A robotic grasping method based on deep learning[J]. Robot, 2017, 39(6): 820-828, 837.
 Wu X R, Huang G M, Sun L N. Fast visual identifification and location algorithm for industrial sorting robots based on deep learning[J]. Robot, 2016, 38(6): 711-719.
 Luna R, Moll M, Badger J, et al. A scalable motion planner for high-dimensional kinematic systems [J]. The International Journal of Robotics Research, 2020, 39(4):361-388.
 McMahon T, Thomas S, Amato N M. Sampling-based motion planning with reachable volumes for high-degree-of-freedom manipulators [J]. The International Journal of Robotics Research, 2018, 37(7): 779-817.
 Solovey K, Salzman O, Halperin D. Finding a needle in an exponential haystack: discrete RRT for exploration of implicit roadmaps in multi-robot motion planning [J]. The International Journal of Robotics Research, 2016, 35(5): 501-513.
 Niku S B. Introduction to Robotics [М]. Upper SaddleRiver: Prentice Hall Professional Technical Reference, 2001. 103-18
 Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning[J]. arXiv preprint arXiv:1509.02971, 2015.