Skip to main content

Design of a Conflict Prediction Algorithm for Industrial Robot Automatic Cooperation


There are communication conflicts in traditional industrial robot cooperation, the lack unified standards for industrial robot signal to automatic predict. For this purpose, a conflict prediction algorithm for the cooperation of industrial robots is designed. The early signal characteristics of industrial robots conflicts in cooperation are analyzed, the chaotic characteristics before and after the collision are obtained. A fuzzy rule for conflict prediction is designed by taking chaos as feature descriptor. By using the fuzzy feature of chaos change of robot data, combined with fuzzy, the conflicts that may exist in robots cooperation are predicted to ensure that the robots can complete tasks without reducing service availability. Conflict prediction (QPME) tool is used to compare the distribution of conflict types and the prediction of different conflict rates associated with different recovery strategies. The experimental results show that the throughput of robot cooperative task increases to 92%, the response time decreases to 0.11 s, the node utilization rate increases to 80%, and the task loss rate decreases to 5%. It can quantitatively analyze the impact of hardware and software conflicts on robot cooperation performance, and predict the conflicts in robot cooperation without affecting robot cooperation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Rao DC, ManasRanjanKabat PKD et al (2019) Hybrid IWD-DE: a novel approach to model cooperative navigation planning for multi-robot in unknown dynamic environment. J Bionic Eng 16(2):235–252

    Article  Google Scholar 

  2. 2.

    Shuai W, Xinyu L, Shuai L et al (2021, online first) Human Short-Long Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities. IEEE Internet Things J.

  3. 3.

    Ji-sheng Z (2020) Multimedia image mining method based on fuzzy pixels difference iterative clustering. Comp Inform Mech Syst 3:114–119

    Google Scholar 

  4. 4.

    Duan Y, Li J, Srivastava G, Yeh JH (2020) Data storage security for the internet of things. J Supercomput 76:8529–8574

    Article  Google Scholar 

  5. 5.

    Schmidtler J, Bengler K (2017) Influence of size-weight illusion on usability in haptic human-robot collaboration. IEEE Trans Haptics 11(1):85–96

    Article  Google Scholar 

  6. 6.

    Mizanoor Rahman SM (2018) Cyber-physical-social system between a humanoid robot and a virtual human through a shared platform for adaptive agent ecology. IEEE/CAA J Autom Sin 5(1):190–203

    Article  Google Scholar 

  7. 7.

    Ragaglia M, Zanchettin AM, Rocco P (2018) Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements. Mechatronics 55(12):267–281

    Article  Google Scholar 

  8. 8.

    Coupeté E, Moutarde F, Manitsaris S (2018) Multi-users online recognition of technical gestures for natural human-robot collaboration in manufacturing. Auton Robot 43(8):1–17

    Google Scholar 

  9. 9.

    de GeaFernández J, Mronga D, Günther M et al (2017) Multimodal sensor-based whole-body control for human-robot collaboration in industrial settings. Robot Auton Syst 94(14):102–119

    Article  Google Scholar 

  10. 10.

    Shuai L, Chunli G, Fadi A et al (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for IIoT environments. Mech Syst Signal Process 138:106537

    Article  Google Scholar 

  11. 11.

    Wenqing C, Weina F (2019) Negative information filtering algorithm based on text content in multimedia networks. Int J Perform Eng 15(11):3061–3071

    Article  Google Scholar 

  12. 12.

    Salih A, Ma X, Peytchev E. (2017) Implementation of Hybrid Artificial Intelligence Technique to Detect Covert Channels Attack in New Generation Internet Protocol IPv6[M]// Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy. 173–190

  13. 13.

    Liu S, Wang S, Liu X et al (2021) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans Fuzzy Syst 29(1):90–102

    Google Scholar 

  14. 14.

    Zhu X, Srivastava GM, Parizi R (2019) An Efficient Encryption Algorithm for the Security of Sensitive Private Information in Cyber-Physical Systems. Electronics 8:1220

    Article  Google Scholar 

  15. 15.

    Wang C, Zhao Z, Gong L, Zhu L, Liu Z, Cheng X (2018) A distributed anomaly detection system for in-vehicle network using HTM. IEEE ACCESS 6:9091–9098

    Article  Google Scholar 

  16. 16.

    Datsika E, Antonopoulos A, Zorba N et al (2017) Green Cooperative Device–to–Device Communication: a Social–Aware Perspective. IEEE Access 4(29):3697–3707

    Google Scholar 

  17. 17.

    Fu Y (2020) Face recognition using scalable constraints data fusion. Comp Inform Mech Syst 3:120–122

    Google Scholar 

  18. 18.

    Shuai L, Shuai W, Xinyu L, et al, (2021) Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring, IEEE Transactions on Multimedia, online first,

  19. 19.

    Felicia E, Apietu KF, Abdulai J-D, et al. 2018 Prolonging the Lifetime of Wireless Sensor Networks: A Review of Current Techniques Wireless Communications & Mobile Computing, (2):1–23

  20. 20.

    Liu S, Liu D, Srivastava G et al (2021) Overview and methods of correlation filter algorithms in object tracking. Complex Intell Syst.

Download references


This study was supported by science and tech nology plan of Qinghai province Key Research & Development and conversion plan, Qinghai Province,Qinghai (No.2019-GX-170).

Author information



Corresponding author

Correspondence to Xiaochun Cheng.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, K., Cheng, X. Design of a Conflict Prediction Algorithm for Industrial Robot Automatic Cooperation. Mobile Netw Appl (2021).

Download citation


  • Industrial robot
  • Cooperation
  • Conflict prediction
  • Automatic
  • chaos
  • Fuzziness
  • Anti-fuzziness