An Egret Swarm Optimization Based BiRNN Method Approach to Determine the Protective Stopping Time of UR3 Robot Arm

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Abstract
The emergency stop moment is of critical importance for robots to meet legal and industrial safety requirements, protect human health and equipment, and respond quickly in times of crisis. In this study, the emergency stop time of the UR3 robot arm, which is widely used in the industry, was determined according to the axis speed, current value, and temperature values. The emergency stop moment of the robot was determined successfully with the state-of-the-art deep learning models. The performance of the state-of-the-art deep learning models was compared using the most common classification metrics. In addition, the most important hyperparameter values of the Bidirectional Recurrent Neural Network method were determined using the Egret Swarm Optimization Algorithm, which is new in literature. Among all models, the ESOA-BiRNN model was the most successful method with a value of 0.946124 according to the ROC AUC metric. The success of the proposed method is given together with other classification metrics.

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