Accurate motion time prediction of robot point-to-point operations
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Manufacturing systems require accurate information on the cycle times of member machines for efficient scheduling to maximize machine utilization and productivity. The industrial robot is a key element of an automated manufacturing system. Prediction methods of robot motion time have been explored including the Robot Time and Motion Method, the Detailed Robot Time System and the Dynamics Simulation Method. Their prediction accuracy of point-to-point operations needs to be improved. This thesis presents the Accurate Robot Motion-Time (ARM-Time) method eligible for the motion-time prediction of robot point-to-point operations. A modeling and prediction procedure is developed for the ARM-Time method. The modeling procedure starts with data collection. The procedure applies regression analysis, using the ARM-Time model as the candidate model, and identifies the prediction function. This method is implemented on two industrial robots: the IBM 7547 industrial robot and the PUMA 560 industrial robot. Extensive experiments have been conducted to acquire motion times of each joint, which are statistically modeled to build the joint- motion-time functions. These joint models are then used in the robot motion time prediction.