Concept Of Developing Self-Optimizing And SelfLearning Packaging Robot Manipulators

Abstract

This article presents the concept of developing and implementing self-optimizing and self-learning robot manipulators in industrial packaging processes. In modern production environments, there is a growing demand for robot manipulators that can adapt to fluctuations in workload and variations in product types along packaging lines. From this perspective, the article analyzes the development process of a self-learning control system based on machine learning, reinforcement learning, and artificial intelligence algorithms, aimed at enhancing decision-making efficiency. The study experimentally evaluates the manipulator's impact on performance, energy consumption, and product quality. The results show that the use of selfoptimizing algorithms in robot manipulators can significantly improve manufacturing efficiency