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A Human-Robot Dynamic Fusion Safety Algorithm for Collaborative Operations of Cobots

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Abstract

The most popular safety scheme for human-robot collaboration is that the robot will stop and wait at human’s approach. This paper aims to tackle the practically very challenging problem of the balance between efficiency and safety in the safety scheme aforementioned in the intelligent manufacturing industry. The human-robot dynamic fusion algorithm (HRDF) was proposed in this paper to ensure the safety of humans initiatively, in favor of reducing the stop times of the collaborative operations. In this algorithm, the digital dynamics model of human was built and employed to estimate the minimum distance between human and robot in real-time. And the virtual force can be calculated based on the minimum distance above. Subsequently, the value and the time duration of the virtual force were employed to evaluate the safety status of human-robot system. Then, robot can conduct safety planning initiatively according to the safety status. In practice, the average response time taken from the detection of the distance change of human-robot to the adjustment of robot is 0.05s with our algorithm. Above all, the effectiveness of our approach is verified empirically with contact maintenance experiments in a non-stop mode. And the HRDF can maximally increase efficiency by 26.4% compared with the traditional stop-maintenance-restart safety mode.

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References

  1. Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P.: Collaborative manufacturing with physical humancrobot interaction. Robotics and Computer-Integrated Manufacturing 40, 1–13 (2016)

    Article  Google Scholar 

  2. Hashemi-Petroodi, S.E., Thevenin, S., Kovalev, S., Dolgui, A.: Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey. Annual Reviews in Control 49, 264–276 (2020)

    Article  Google Scholar 

  3. Casalino, A., Zanchettin, A.M., Piroddi, L., Rocco, P.: Optimal scheduling of human-robot collaborative assembly operations with time petri nets. IEEE Trans. Autom. Sci. Eng., 1–15 (2019)

  4. Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P.: Collaborative manufacturing with physical humancrobot interaction. Robotics and Computer Integrated Manufacturing 40, 1–13 (2016)

    Article  Google Scholar 

  5. Unhelkar, V.V., Lasota, P.A., Tyroller, Q., Buhai, R.D., Marceau, L., Deml, B., Shah, J.A.: Human-aware robotic assistant for collaborative assembly: Integrating human motion prediction with planning in time. IEEE Robotics and Automation Letters 3(3), 2394–2401 (2018)

    Article  Google Scholar 

  6. Villani, V., Pini, F., Leali, F., Secchi, C.: Survey on humancrobot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics 55, 248–266 (2018)

    Article  Google Scholar 

  7. Goodrich, M.A., Schultz, A.C.: Human-robot interaction: a survey. Foundations & Trends in Human Computer Interaction 1(3), 203–275 (2007)

    Article  Google Scholar 

  8. Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schaeffer, A.: On making robots understand safety: Embedding injury knowledge into control. International Journal of Robotics Research 31(13), 1578–1602 (2012)

    Article  Google Scholar 

  9. Povse, B., Koritnik, D., Bajd, T., Munih, M.: Correlation between impact-energy density and pain intensity during robot-man collision. In: IEEE Ras & Embs International Conference on Biomedical Robotics & Biomechatronics (2010)

  10. Rosenstrauch, M., Kruger, J.: Safe human-robot-collaboration-introduction and experiment using iso/ts 15066. In: International Conference on Control and Automation, pp. 740–744 (2010)

  11. Lee, Y.H., Lee, Y.H., Lee, H., Kang, H., Choi, H.R.: Force-controllable quadruped robot system with capacitive-type joint torque sensor. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)

  12. Xijian, H., Xia, Y., Liu, Y., Jiang, L., Liu, H.: Humanoid arm with the integrated serial elastic actuator. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2014)

  13. Duchaine, V., Lauzier, N., Baril, M., Lacasse, M.A., Gosselin, C.: A flexible robot skin for safe physical human robot interaction. In: IEEE International Conference on Robotics & Automation (2009)

  14. Pang, G., Deng, J., Wang, F., Zhang, J., Pang, Z., Yang, G.: Development of flexible robot skin for safe and natural humancrobot collaboration. Micromachines 9(11), 576 (2018)

    Article  Google Scholar 

  15. Magrini, E., De Luca, A.: Hybrid force/velocity control for physical human-robot collaboration tasks. In: IEEE International Conference on Intelligent Robots & Systems (2016)

  16. Raiola, G., Cardenas, C.A., Tadele, T.S., De Vries, T., Stramigioli, S.: Development of a safety and energy aware impedance controller for collaborative robots. IEEE Robotics & Automation Letters 3(2), 1237–1244 (2018)

    Article  Google Scholar 

  17. Lo, S.Y., Cheng, C.A., Huang, H.P.: Virtual impedance control for safe human-robot interaction. Journal of Intelligent & Robotic Systems 82(1), 3–19 (2016)

    Article  Google Scholar 

  18. Yanan, L., Sam, G.S.: Force tracking control for motion synchronization in human-robot collaboration. Robotica 34(6), 1260–1281 (2016)

    Article  Google Scholar 

  19. Navarro, B., Cherubini, A., Fonte, A., Passama, R., Fraisse, P.: An iso10218-compliant adaptive damping controller for safe physical human-robot interaction. In: IEEE International Conference on Robotics & Automation (2016)

  20. Sadeghian, H., Keshmiri, M., Villani, L., Siciliano, B.: Null-space impedance control with disturbance observer. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)

  21. Yu, X., Zhang, S., Sun, L., Wang, Y., Li, B.: Cooperative control of dual-arm robots in different human-robot collaborative tasks. Assembly Automation 40(1), 95–104 (2019)

    Article  Google Scholar 

  22. Keemink, A.Q., Herman, V.D.K., Ha, S.A.: Admittance control for physical humancrobot interaction. The International Journal of Robotics Research 37(11), 1421–1444 (2018)

    Article  Google Scholar 

  23. Dimeas, F., Aspragathos, N.: Online stability in human-robot cooperation with admittance control. IEEE Transactions on Haptics 9(2), 267–278 (2016)

    Article  Google Scholar 

  24. Landi, C.T., Ferraguti, F., Sabattini, L., Secchi, C., Fantuzzi, C.: Admittance control parameter adaptation for physical human-robot interaction. In: International Conference on Robotics and Automation, pp. 2911–2916 (2017)

  25. Dimeas, F., Aspragathos, N.A.: Fuzzy learning variable admittance control for human-robot cooperation. In: Intelligent Robots and Systems, pp. 4770–4775 (2014)

  26. Tan, J.T., Duan, F., Kato, R., Arai, T.: Safety strategy for humancrobot collaboration: Design and development in cellular manufacturing. Advanced Robotics 24(10), 839–860 (2010)

    Article  Google Scholar 

  27. Kostavelis, I., Kargakos, A., Giakoumis, D., Tzovaras, D.: Robots workspace enhancement with dynamic human presence for socially-aware navigation. In: International Conference on Computer Vision Systems, pp. 279–288 (2017)

  28. Flacco, F., Kroger, T., De Luca, A., Khatib, O.: A depth space approach to human-robot collision avoidance. In: International Conference on Robotics and Automation, pp. 338–345 (2012)

  29. Sprunk, C., Lau, B., Pfaff, P., Burgard, W.: An accurate and efficient navigation system for omnidirectional robots in industrial environments. Autonomous Robots 41(2), 473–493 (2017)

    Article  Google Scholar 

  30. Flacco, F., Kroeger, T., De Luca, T., Khatib, O.: A depth space approach for evaluating distance to objects. Journal of Intelligent and Robotic Systems 80(1), 7–22 (2015)

    Article  Google Scholar 

  31. Marvel, J.A.: Performance metrics of speed and separation monitoring in shared workspaces. IEEE Transactions on Automation Science and Engineering 10(2), 405–414 (2013)

    Article  Google Scholar 

  32. Vicentini, F., Giussani, M., Tosatti, L.M.: Trajectory-dependent safe distances in human-robot interaction. In: Emerging Technologies and Factory Automation, pp. 1–4 (2014)

  33. Lasota, P.A., Rossano, G., Shah, J.A.: Toward safe close-proximity human-robot interaction with standard industrial robots. In: Conference on Automation Science and Engineering, pp. 339–344 (2014)

  34. Mohammed, A., Schmidt, B., Wang, L.: Active collision avoidance for humancrobot collaboration driven by vision sensors. International Journal of Computer Integrated Manufacturing 30(9), 970–980 (2017)

    Article  Google Scholar 

  35. Hoffman, S.G.: Whole-body postures during standing hand-force exertions: Development of a three-dimensional biomechanical posture prediction model. PhD thesis (2008)

  36. Luo, R., Hayne, R., Berenson, D.: Unsupervised early prediction of human reaching for humancrobot collaboration in shared workspaces. Autonomous Robots 42(3), 631–648 (2018)

    Article  Google Scholar 

  37. Shi, H., Chen, J., Pan, W., Hwang, K., Cho, Y.: Collision avoidance for redundant robots in position-based visual servoing. IEEE Systems Journal 13(3), 3479–3489 (2019)

    Article  Google Scholar 

  38. Real-time urdf filter. https://github.com/blodow/realtime_urdf_filter. Accessed 26 Apr 2019

  39. Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: Openpose: Realtime multi-person 2d pose estimation using part affinity fields. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1 (2019)

  40. Perezdarpino, C., Shah, J.A.: Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. In: International Conference on Robotics and Automation, pp. 6175–6182 (2015)

  41. Zhu, L., Chi, Z., Zhou, F., Zhuang, C.: Dynamic motion planning algorithm in human-robot collision avoidance. In: International Conference on Intelligent Robotics and Applications, pp. 655–666 (2019)

  42. Magrini, E., Ferraguti, F., Ronga, A.J., Pin, A.J., De Luca, A., Leali, A.: Human-robot coexistence and interaction in open industrial cells. Robotics and Computer-Integrated Manufacturing 61, 1–19 (2020)

    Article  Google Scholar 

  43. Morato, C., Kaipa, K.N., Zhao, B., Gupta, S.K.: Toward safe human robot collaboration by using multiple kinects based real-time human tracking. Journal of Computing and Information Science in Engineering 14(1), 011006 (2014)

    Article  Google Scholar 

  44. Zhou, Y., Dong, H., El Saddik, A.: Learning to estimate 3d human pose from point cloud. IEEE Sensors Journal 99, 1 (2020)

    Google Scholar 

  45. Chang, J.Y., Moon, G., Lee, K.M.: V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map. In: Computer Vision and Pattern Recognition, pp. 5079–5088 (2018)

  46. Reily, B., Han, F., Parker, L.E., Zhang, H.: Skeleton-based bio-inspired human activity prediction for real-time humancrobot interaction. Autonomous Robots 42(6), 1281–1298 (2018)

    Article  Google Scholar 

  47. Rajnathsing, H., Li, C.: A neural network based monitoring system for safety in shared work-space human-robot collaboration. Industrial Robot-an International Journal 45(4), 481–491 (2018)

    Article  Google Scholar 

  48. Nikolakis, N., Maratos, V., Makris, S.: A cyber physical system (cps) approach for safe human-robot collaboration in a shared workplace. Robotics and Computer-integrated Manufacturing 56, 233–243 (2019)

    Article  Google Scholar 

  49. Khalid, A., Kirisci, P.T., Khan, Z.H., Ghrairi, Z., Thoben, K., Pannek, J.: Security framework for industrial collaborative robotic cyber-physical systems. Computers in Industry 97, 132–145 (2018)

    Article  Google Scholar 

  50. Shiomi, M., Shatani, K., Minato, H.: Does a robot’s subtle pause in reaction time to people’s touch contribute to positive influences? In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 364–369 (2018)

  51. Li, S., Zhang, S., Fu, Y., Xiong, Y., Xie, Z.: Grasp2hardness: fuzzy hardness inference of cylindrical objects for grasp force adjustment of force sensor-less robots. Intelligent Service Robotics 14(2), 129–141 (2021)

    Article  Google Scholar 

  52. Li, S., Zhang, S., Fu, Y., Wang, H., Han, K.: Task-based obstacle avoidance for uncertain targets based on semantic object matrix. Control Engineering Practice 105, 104649 (2020)

    Article  Google Scholar 

  53. Featherstone, R., Orin, D.: Robot dynamics: equations and algorithms. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), vol. 1, pp. 826–834 (2000)

  54. Zheng, K.A., Hu, Y., Yu, W.A.: A novel parallel recursive dynamics modeling method for robot with flexible bar-groups. Applied Mathematical Modelling 77, 267–288 (2020)

    Article  MathSciNet  Google Scholar 

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Funding

We gratefully acknowledge the financial support that Shiqi Li and Shuai Zhang received from the HUST & UBTECH Intelligent Service Robots Joint Lab.

Author information

Authors and Affiliations

  1. Center for Psychological Sciences, Zhejiang University, 310058, Hangzhou, China

    Shuai Zhang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China

    Shiqi Li & Xiao Li

  3. HUST&UBTECH Intelligent Service Robots Joint Lab, Huazhong University of Science and Technology, 430074, Wuhan, China

    Xiao Li

  4. UBTECH Robotics CORP LTD, 518000, Shenzhen, China

    Youjun Xiong & Zheng Xie

Authors
  1. Shuai Zhang
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  2. Shiqi Li
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  3. Xiao Li
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  4. Youjun Xiong
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  5. Zheng Xie
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Contributions

Shuai Zhang and Shiqi Li conceived and designed the study. Shuai Zhang and Xiao Li performed the experiments. Youjun Xiong And Zheng Xie provided the robot. Shuai Zhang wrote the paper. Shuai Zhang, Shiqi Li and Youjun Xiong reviewed and edited the manuscript. All authors read and approved the manuscript. Most of the work of this paper were accomplish by Shuai Zhang in the period of pursuing the Ph.D. degree at Huazhong University of Science and Technology. And the revised work of this paper were done by Shuai Zhang when he does postdoctoral research at the the Center for Psychological Sciences, Zhejiang University.

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Correspondence to Shiqi Li.

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Zhang, S., Li, S., Li, X. et al. A Human-Robot Dynamic Fusion Safety Algorithm for Collaborative Operations of Cobots. J Intell Robot Syst 104, 18 (2022). https://doi.org/10.1007/s10846-021-01534-8

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