MANUFACTURING PERFORMANCE INCREASE BY APPLICATION OF A TRANSFORMABLE ASSEMBLY SYSTEM USING COLLABORATIVE ROBOTICS
Abstract
The article considers the concept of a transformable assembly system with the use of collabora-tive robotics. The article describes the paradigm of transformable assembly systems aimed at creating the basis for autonomous context-dependent and adaptable assembly systems that can be developed together with products, processes, and business and social environment. In transformable assembly systems, this property is achieved by contextual adaptation of actuators with decentralized multi-agent control system. One of the key technologies in the development of the transformable assembly system under study is the use of collaborative robotic complexes. The peculiarities of forming the principles of collaborative robotic systems with multi-agent control structure are shown. The property of collabora-tion is considered in the sense of interaction between deterministic agents - robots and non-deterministic agents - people within the framework of the described environment, where agents separate a single space and objects in the performance of joint tasks. Within the framework of the system under study the concept of partial operational automation of aircraft hull structures assembly is proposed. The essence of this solution lies in the joint work of a man and a collaborative robot within one technological process - drilling and riveting. Such tasks as primary analysis and concept detailing, development of simu-lation modeling apparatus on the basis of game model, formation of conclusions about the obtained results and their interpretation to determine the direction of further research are solved. Such a combi-nation allowed to reduce the total time on the operation and reduce their total labor intensity with min-imal interference in the existing technological process.
References
2. Wang L., Törngren M., Onori M. Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, 2015, Vol. 37, pp. 517-527.
3. Colgate J., Peshkin M. US Patent No. 5952796: Cobots, Patent, September, 1999.
4. Maurtua I., Ibarguren A., Kildal J., Susperregi L., Sierra B. Human–robot collaboration in industrial applications: Safety, interaction and trust, International Journal of Advanced Robot-ic Systems, 2017, Vol. 14, No. 4, pp. 1-10.
5. Rosenstrauch M.J., & Krüger J. Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066, In 2017 3rd International Conference on Control, Automation and Ro-botics (ICCAR), April 2017, pp. 740-744. IEEE. 6. Cherubini A., Passama R., Crosnier A., Lasnier A., Fraisse P. Collaborative manufacturing with physical human–robot interaction, Robotics and Computer-Integrated Manufacturing, 2016, Vol. 40, pp. 1-13.
7. Bütepage J., Kragic D. Human-robot collaboration: from psychology to social robotics, arXiv preprint arXiv:1705.10146, 2017.
8. Vorotnikov S. et al. Multi-agent Robotic Systems in Collaborative Robotics, International Conference on Interactive Collaborative Robotics. Springer, Cham, 2018, pp. 270-279.
9. Drouot A., Irving L., Sanderson D., Smith A., Ratchev S. A transformable manufacturing con-cept for low-volume aerospace assembly, IFAC-PapersOnLine, 2017, Vol. 50, No. 1, pp. 5712-5717.
10. Vashukov Yu.A., Lomovskoy O.V., Sharov A.A. Tekhnologiya i oborudovanie sborochnykh protsessov [Technology and equipment for assembly processes]. Samara, 2011. Available at: https://docplayer.ru/31668726-Yu-a-vashukov-o-v-lomovskoy-a-a-sharov.html, svobodnyy (accessed 15 February 2019).
11. Shi Z. et al. New design of a compact aero-robotic drilling end effector: An experimental anal-ysis, Chinese Journal of Aeronautics, 2016, Vol. 29, No. 4, pp. 1132-1141.
12. Pang G. et al. Development of flexible robot skin for safe and natural human–robot collabora-tion, Micromachines, 2018, Vol. 9, No. 11, pp. 576.
13. Mazzocchi T. et al. Smart sensorized polymeric skin for safe robot collision and environmental interaction, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015, pp. 837-843.
14. Volodin S.Y., Mikhaylov B.B., Yuschenko A.S. Autonomous robot control in partially undeter-mined world via fuzzy logic, Advances on Theory and Practice of Robots and Manipulators. Springer, Cham, 2014, pp. 197-203.
15. Grigore E.C. et al. Predicting supportive behaviors for human-robot collaboration, Proceed-ings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. In-ternational Foundation for Autonomous Agents and Multiagent Systems, 2018, pp. 2186-2188.
16. Myerson R.B. Game theory. Harvard university press, 2013.
17. Shannon R.E., Long S.S., Buckles B.P. Operation research methodologies in industrial engi-neering: A survey, AIIE Transactions, 1980, Vol. 12, No. 4, pp. 364-367.
18. Nikolaidis S. et al. Game-theoretic modeling of human adaptation in human-robot collabora-tion, 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI. – IEEE, 2017), pp. 323-331.
19. Sadrfaridpour B. et al. Modeling and control of trust in human-robot collaborative manufac-turing, Robust Intelligence and Trust in Autonomous Systems. Springer, Boston, MA, 2016, pp. 115-141.
20. Wang X.V. et al. Human–robot collaborative assembly in cyber-physical production: Classifi-cation framework and implementation, CIRP annals, 2017, Vol. 66, No. 1, pp. 5-8.
