Research on the structural design of exoskeleton assisted transport robot combined with reinforcement learning algorithm under the background of artificial intelligence
Abstract
With the comprehensive interface between “Made in China 2025” and Industry 4.0, the handling mode of the handling system is constantly updated and developed, and a new type of handling mode, which is assisted by exoskeleton and other equipments to complete the handling work of workers, has been gradually applied. However, most of the existing exoskeleton-assisted robots are expensive and complicated in structure, which are not applicable to the actual needs of ordinary workers. Therefore, it is of great significance to design an exoskeleton-assisted handling robot that is applicable to the needs of ordinary workers. Based on this, this paper designs a relatively simple structure and low cost exoskeleton-assisted handling robot, and introduces the BN-Q-learning algorithm to give the control strategy of the robot, and finally simulates and analyzes the reliability of the handling robot, and the results show that the exoskeleton-assisted handling robot designed in this paper has a high reliability, and the force situation and the human body’s joints are relatively well matched when handling heavy objects. The results show that the exoskeleton assisted handling robot designed in this paper is highly reliable, and the force situation when handling heavy objects matches the human body joints.
References
1. Yamamoto K, Ishii M, Noborisaka H, et al. Stand alone wearable power assisting suit-sensing and control systems. In: Proceedings of the RO-MAN 2004 13th IEEE International Workshop on Robot and Human Interactive Communication; 2004. pp. 661–66.
2. Naito J, Obinata G, Nakayama A, et al. Development of a Wearable Robot for Assisting Carpentry Workers. International Journal of Advanced Robotic Systems. 2007; 4(4). doi: 10.5772/5667
3. Ryu HT, Choi JY, Yi BJ. Human-robot integrated model of upper-extremity. International Conference on Ubiquitous Robots and Ambient Intelligence. IEEE. 2012; 7–9.
4. Yu ZF. Simulation of Fuzzy Adaptive PID in lower extremity exoskeleton assisted leg. Xiamen University; 2014.
5. Yu H, Choi IS, Han KL, et al. Development of a Stand-alone Powered Exoskeleton Robot Suit in Steel Manufacturing. ISIJ International. 2015; 55(12): 2609–2617. doi: 10.2355/isijinternational.isijint-2015-272
6. Zhao H, Mo X, Ji P. Optimal design of sensing boots for lower extremity Power exoskeleton suit. Microcontroller and Embedded System Application. 2015; 15(2): 56–59.
7. Gao K, Liu H, Chen S. Structural Design and Simulation of Exoskeleton Assisted Handling Robot Based on Virtual Prototype Technology. Mechanical and Electrical Engineering. 2018; 35(04): 391–396.
8. Mauri A, Lettori J, Fusi G, et al. Mechanical and Control Design of an Industrial Exoskeleton for Advanced Human Empowering in Heavy Parts Manipulation Tasks. Robotics. 2019; 8(3): 65. doi: 10.3390/robotics8030065
9. Ning Y, Li Y, Zhang Z, et al. Design and Analysis of Upper Limb Passive Exoskeleton Robot for Lift Assist. Journal of Physics: Conference Series. 2022; 2410(1): 012020. doi: 10.1088/1742-6596/2410/1/012020
10. Malyuga OV. The kinematic structure of the mechanism of the exoskeleton. World Ecology Journal. 2017; 7(11): 3–10.
11. Menon S. Structural design and analysis of exoskeleton handling robot based on virtual simulation technology in artificial intelligence environment. The journal of contemporary issues in business and government. 2022; 28(4): 2046–2056.
12. Lim D, Kim W, Lee H, et al. Development of a lower extremity exoskeleton robot with a quasi-anthropomorphic design approach for load carriage. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2015. pp. 5345–5350.
13. Song Z, Tian C, Dai JS. Mechanism design and analysis of a proposed wheelchair-exoskeleton hybrid robot for assisting human movement. Mechanical Sciences. 2019; 10(1): 11–24. doi: 10.5194/ms-10-11-2019
14. Zhang R, Zhu Y, Li H, et al. Development of a parallel-structured upper limb exoskeleton for lifting assistance. In: Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM); 2019. pp. 307–312.
15. Qiu S, Pei Z, Wang C, et al. Systematic Review on Wearable Lower Extremity Robotic Exoskeletons for Assisted Locomotion. Journal of Bionic Engineering. 2022; 20(2): 436–469. doi: 10.1007/s42235-022-00289-8
16. Hofman J, De Backer P, Manghi I, et al. First-in-human real-time AI-assisted instrument deocclusion during augmented reality robotic surgery. Healthcare Technology Letters. 2023; 11(2–3): 33–39. doi: 10.1049/htl2.12056
17. Haidegger T, Speidel S, Stoyanov D, et al. Robot-Assisted Minimally Invasive Surgery—Surgical Robotics in the Data Age. Proceedings of the IEEE. 2022; 110(7): 835–846. doi: 10.1109/jproc.2022.3180350
18. Zhu Z, Ng DWH, Park HS, et al. 3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies. Nature Reviews Materials. 2020; 6(1): 27–47. doi: 10.1038/s41578-020-00235-2
19. Chiou M, Epsimos GT, Nikolaou G, et al. Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise. In: Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2022. pp. 4545–4552.
20. Goh QL, Chee PS, Lim EH, et al. An AI-assisted and self-powered smart robotic gripper based on eco-egain nanocomposite for pick-and-place operation. Nanomaterials. 2022; 12(8): 1317.
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