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.

传统工业机器人协作存在通信冲突，缺乏统一的工业机器人信号自动预测标准。为此，设计了一种工业机器人协作的冲突预测算法。分析了工业机器人协作冲突的早期信号特征，得到了碰撞前后的混沌特征。以混沌为特征描述子设计了冲突预测的模糊规则。利用机器人数据混沌变化的模糊特征，结合模糊预测机器人合作中可能存在的冲突，保证机器人在不降低服务可用性的情况下完成任务。冲突预测（QPME）工具用于比较冲突类型的分布以及与不同恢复策略相​​关的不同冲突率的预测。实验结果表明，机器人协作任务吞吐量提高到92%，响应时间降低到0.11 s，节点利用率提高到80%，任务丢失率降低到5%。可以定量分析软硬件冲突对机器人协作性能的影响，在不影响机器人协作的情况下预测机器人协作中的冲突。任务丢失率降低到5%。可以定量分析软硬件冲突对机器人协作性能的影响，在不影响机器人协作的情况下预测机器人协作中的冲突。任务丢失率降低到5%。可以定量分析软硬件冲突对机器人协作性能的影响，在不影响机器人协作的情况下预测机器人协作中的冲突。