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Design of a Conflict Prediction Algorithm for Industrial Robot Automatic Cooperation

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Abstract

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.

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Acknowledgements

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).

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Authors and Affiliations

  1. School of Physics and Electronic Information Engineering, Qinghai Nationalities University, Xining, China

    Kaiyong Li

  2. Deparementof Computer Science, Middlesex University, London, UK

    Xiaochun Cheng

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  1. Kaiyong Li
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  2. Xiaochun Cheng
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Correspondence to Xiaochun Cheng.

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Li, K., Cheng, X. Design of a Conflict Prediction Algorithm for Industrial Robot Automatic Cooperation. Mobile Netw Appl 27, 535–546 (2022). https://doi.org/10.1007/s11036-021-01819-0

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  • DOI: https://doi.org/10.1007/s11036-021-01819-0

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