Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems

With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model's exceptional performance in yarn-spool-assembly tasks. Compared to traditional reinforcement learning methods, our model shows potential advantages in improving task success rates and reducing collision rates. The cascaded fuzzy reward system, a core component of our end-to-end deep reinforcement learning model, offers a novel and more robust solution for the automated path planning of robotic arms. In summary, the method proposed in this study provides a new perspective and potential applications for industrial automation, especially in the operation of robotic arms in complex and uncertain environments.