Cross-fold deep learning for improving robot task execution precision in smart industries

Robot-optimized process control in innovative industries relies on extensive data filtering and analysis to improve task-specific precision. Based on the remote instructions and task completion, the control dissemination must be swift and precise. To enhance the accuracy of robot control dissemination, this article introduces a Task Cooperative Control Dissemination Scheme (TC2DS). This scheme aims to improve the synchronization between control dissemination and task completion at regular mapping intervals. The smart industry tasks are scheduled based on first-come, first-served completion time intervals to improve precision. This scheduling is for which control dissemination is validated using a cross-fold deep learning paradigm. In this cross-fold process, learning for task completion, synchronization, and failure pairs is performed. The learning process verifies the connectivity between the above pairs; the connected task allocation/completion intervals achieve less precision than is used for training. Through this training process, the synchronization controls are rescheduled to prevent further failures. Therefore, multiple robot task pending instances are prevented in this scheme.