Maestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines

Manufacturing lines face numerous challenges in task recognition and optimization, particularly due to their dynamic nature. To tackle these issues, we introduce Maestro, a multi-agent enhanced system that utilizes a decentralized agent architecture. Each agent within Maestro specializes in specific facets of the manufacturing process, which fosters efficient collaboration and data sharing. By employing machine learning algorithms, Maestro dynamically recognizes tasks, allowing it to adapt to real-time fluctuations in manufacturing conditions. Furthermore, this system merges task recognition with advanced optimization algorithms, significantly enhancing production efficiency and minimizing downtime. Comprehensive simulations and experiments conducted across various manufacturing environments validate the framework, revealing marked improvements in task completion rates and resource utilization. Maestro stands as a pivotal advancement in creating a more agile and intelligent manufacturing ecosystem.