Abstract
The trend of short-run production and personalized customization is more and more popular in the manufacturing industry. And the robots in these production lines must conduct task adjustment efficiently when learning new tasks. Thus, this paper developed the intelligent manufacturing cell based on the human–robot collaboration (HRC-IMC) which can enhance the learning ability of cobots by introducing the intelligence of human. The HRC-IMC was composed with four modules: the imitating learning module, the human–robot safety planning module, the task planning module and the visual inferring module. All of the four modules were designed to provide a set of systematic and effective methods. That was conductive to the efficiency improvement of the task adjustment for cobots’ new task learning. The experimental results indicated that the efficiency of task adjustment can be increased by 42.8 % when the HRC-IMC was employed than that of Moveit. All in all, this study is of great significance for improving the efficiency of new task adjustment of cobots by imitating the manipulation experience of human via combining four algorithm modules.
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References
Zhong R, Xun X, Klotz E, Newman S (2017) Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5):616–630
Zhou J, Li P, Zhou Y, Wang B, Meng L (2018) Toward new-generation intelligent manufacturing. Engineering 4(4):11–20
Zhu H, Gupta A, Rajeswaran A, Levine S, Kumar V (2019) Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, p 3651–3657
Karami A, Sadeghian H, Keshmiri M, Oriolo G (2018) Hierarchical tracking task control in redundant manipulators with compliance control in the null-space. Mechatronics 55:171–179
Sheng L, Bao L, Wu P (2018) Application of heuristic approaches in the robot path planning and optimization: a review. Electron Opt Control
Raessa M, Chen J, Wan W, Harada K (2020) Human-in-the-loop robotic manipulation planning for collaborative assembly. arXiv 17(4):1800–1814
Wang X, Kemny Z, Vncza J, Wang L (2017) Human robot collaborative assembly in cyber-physical production: Classification framework and implementation. Cirp Ann Manuf Technol 66(1):5–8
Weitschat R, Aschemann H (2018) Safe and efficient human crobot collaboration part ii: Optimal generalized human-in-the-loop real-time motion generation. IEEE Robot Automation Lett 3(4):3781–3789
Music S, Salvietti G, Dohmann P, Chinello F, Hirche S (2019) Human–robot team interaction through wearable haptics for cooperative manipulation. IEEE Trans Haptics 12(3):350–362
Rahman S (2019) Cognitive cyber-physical system (c-cps) for human–robot collaborative manufacturing. In: 2019 14th Annual Conference System of Systems Engineering (SoSE)
Nunes D, Silva J, Boavida F (2018) A practical introduction to human-in-the-loop cyber-physical systems. John Wiley & Sons, Hoboken
Schirner G, Erdogmus D, Chowdhury K, Padir T (2013) The future of human-in-the-loop cyber-physical systems. Computer 46(1):36–45
Sowe S, Simmon E, Zettsu K, Vaulx F, Bojanova I (2016) Cyber-physical-human systems: Putting people in the loop. IT Professional 18(1):10–13
Krugh M, Mears L (2018) A complementary cyber-human systems framework for industry 4.0 cyber-physical systems. Manuf Lett 15:89–92
Zhou J, Zhou Y, Wang B, Zang J (2019) Human cyber physical systems (hcpss) in the context of new-generation intelligent manufacturing. Engineering
Wang L, Gao R, Vncza J, Krger J, Chryssolouris G (2019) Symbiotic human–robot collaborative assembly. CIRP Ann - Manuf Technol 68(2):701–726
Anupma Y, Jayswal S (2018) Modelling of flexible manufacturing system: a review. Int J Prod Res 56(7–8):2464–2487
Ajoudani A, Zanchettin AM, Ivaldi S, Albu-Schffer A, Kosuge K, Khatib O (2017) Progress and prospects of the human–robot collaboration. Auton Robots 42(5):957–975
Maurtua I, Ibarguren A, Kildal J, Susperregi L, Sierra B (2017) Human–robot collaboration in industrial applications: Safety, interaction and trust. Int J Adv Robot Syst 14(4):1–10
Ioannis K, Andreas K, Dimitrios G, Dimitrios T (2017) Robot’s workspace enhancement with dynamic human presence for socially-aware navigation. In: International Conference on Computer Vision Systems, p 279–288
Christoph S, Boris L, Patrick P, Wolfram B (2017) An accurate and efficient navigation system for omnidirectional robots in industrial environments. Auton Robots 41(2):473–493
Lotsaris K, Fousekis N, Koukas S, Aivaliotis S, Makris S (2021) Augmented reality (ar) based framework for supporting human workers in flexible manufacturing. Proc CIRP 96:301–306
Fabrizio F, Torsten K, Alessandro D, Oussama K (2015) A depth space approach for evaluating distance to objects. J Intell Robot Syst 80(1):7–22
Emanuele M, Federica F, Jacopo R, Fabio P, Alessandro D, Francesco L (2020) Human–robot coexistence and interaction in open industrial cells. Robot Comput-Integr Manuf 61:1–19
Nikolaos N, Vasilis M, Sotiris M (2019) A cyber physical system (cps) approach for safe human–robot collaboration in a shared workplace. Robot Comput-Integr Manuf 56:233–243
Azfar K, Kirisci P, Khan Z, Zied G, Klausdieter T, Jurgen P (2018) Security framework for industrial collaborative robotic cyber-physical systems. Comput Ind 97:132–145
Michalos G, Kousi N, Karagiannis P, Gkournelos C, Dimoulas K, Koukas S, Mparis K, Papavasileiou A, Makris S (2018) Seamless human robot collaborative assembly an automotive case study. Mechatronics 55
Aivaliotis P, Aivaliotis S, Gkournelos C, Kokkalis K, Michalos G, Makris S (2019) Power and force limiting on industrial robots for human–robot collaboration. Robot Comput-Integr Manuf 59:346–360
Zhou Y, Dong H, Saddik A (2020) Learning to estimate 3d human pose from point cloud. IEEE Sens J 99:1–1
Shi H, Chen J, Pan W, Hwang K, Cho Y (2019) Collision avoidance for redundant robots in position-based visual servoing. IEEE Syst J 13(3):3479–3489
Zhu L, Chi Z, Zhou F, Zhuang C (2019) Dynamic motion planning algorithm in human–robot collision avoidance. In: International Conference on Intelligent Robotics and Applications, p 655–666
Edmonds M, Gao F, Xie X, Liu H, Qi S, Zhu Y, Rothrock B, Zhu S (2017) Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp 3530–3537
Pham T, Nikolaos K, Argyros A, Abderrahmane K (2017) Hand-object contact force estimation from markerless visual tracking. IEEE Trans Patt Analysis Mach Intell 40(12):2883–2896
Pham T, Abderrahmane K, Ammar Q, Argyros A (2015) Towards force sensing from vision: Observing hand-object interactions to infer manipulation forces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p 2810–2819
Omid T, Nima G, Black M, Dimitrios T (2020) GRAB: a dataset of whole-body human grasping of objects. In: European Conference on Computer Vision (ECCV)
Brahmbhatt S, Tang C, Twigg C, Kemp C, James H (2020) Contactpose: a dataset of grasps with object contact and hand pose. arXiv preprint: 2007.09545v1
Chang G, Kulic D (2013) Robot task learning from demonstration using petri nets. In: RO-MAN, 2013 IEEE, p 31–36
Casalino A, Cividini F, Zanchettin A, Piroddi L, Rocco P (2018) Human–robot collaborative assembly: a use-case application - sciencedirect. IFAC-PapersOnLine 51(11):194–199
Dantam N, Kingston Z, Chaudhuri S, Kavraki L (2016) Incremental task and motion planning: a constraint-based approach. Robot: Sci Syst 12:1–6
Dantam N, Kingston Z, Chaudhuri S (2018) An incremental constraint-based framework for task and motion planning. Int J Robot Res 37(10):1134–1151
Evangelou G, Dimitropoulos N, Michalos G, Makris S (2021) An approach for task and action planning in human collaborative cells using AI. Proc CIRP
Tsarouchi P, Matthaiakis A, Makris S, George C (2017) On a human–robot collaboration in an assembly cell. Int J Comput Integr Manuf 30(6):580–589
Li S, Zhang S, Fu Y, Wang H, Han K (2020) Task-based obstacle avoidance for uncertain targets based on semantic object matrix. Control Eng Pract 105:104649
Li S, Zhang S, Fu Y, Xiong Y, Xie Z (2021) Grasp2hardness: Fuzzy hardness inference of cylindrical objects for grasp force adjustment of force sensor-less robots. Intell Serv Robot 14(2):129–141
Pan J, Sachin C, Dinesh M (2012) Fcl: a general purpose library for collision and proximity queries. In: 2012 IEEE International Conference on Robotics and Automation, p 3859–3866
Morgan Q, Ken C, Brian G, Josh F, Tully F, Jeremy L, Rob W, Andrew N (2009) Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software (vol. 3), Kobe, Japan, p 5
Gary B (2000) The opencv library. Dr. Dobb’s J: Softw Tools Professional Programmer 25(11):120–123
Bogdan R, Cousins S (2011) 3D is here: Point cloud library (PCL). In: 2011 IEEE International Conference on Robotics and Automation, IEEE, p 1–4
Martín A, Paul B, Chen J, Chen Z, Davis A, Jeffrey D, Matthieu D, Sanjay G, Geoffrey I, Michael I (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), p 265–283
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We acknowledge the support received from the HUST & UBTECH Intelligent Service Robots Joint Lab.
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Shuai Zhang and Shiqi Li conceived and designed the study. Shuai Zhang and Xiao Li performed the experiments. Shuai Zhang wrote the paper. Shuai Zhang, Shiqi Li and Haipeng Wang reviewed and edited the manuscript. All authors read and approved the manuscript.
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Zhang, S., Li, S., Wang, H. et al. An intelligent manufacturing cell based on human–robot collaboration of frequent task learning for flexible manufacturing. Int J Adv Manuf Technol 120, 5725–5740 (2022). https://doi.org/10.1007/s00170-022-09005-6
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DOI: https://doi.org/10.1007/s00170-022-09005-6