Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work investigates automated secondary robotic food packaging solutions that transfer food products from the conveyor belt into containers. A major problem in these solutions is varying product supply which can cause drastic productivity drops. Conventional rule-based approaches, used to address this issue, are often inadequate, leading to violation of the industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning responsive and predictive policy, based on experience. However, it is challenging to utilize it in highly complex control schemes. In this paper, we propose a reinforcement learning framework, designed to optimize the conveyor belt speed while minimizing interference with the rest of the control system. When tested on real-world data, the framework exceeds the performance requirements (99.8% packed products) and maintains quality (100% filled boxes). Compared to the existing solution, our proposed framework improves productivity, has smoother control, and reduces computation time.

翻译：智能制造因日益增长的生产率与灵活性最大化、同时减少浪费与交付周期的需求而变得愈发重要。本研究探究了自动化二次食品包装机器人解决方案，该方案将食品从传送带转移至容器中。这类方案面临的主要问题是产品供应的波动性，这可能导致生产率急剧下降。用于解决此问题的传统基于规则的方法往往效果不佳，甚至违反行业要求。相比之下，强化学习具有通过基于经验学习响应性与预测性策略来解决该问题的潜力。然而，在高复杂控制方案中应用强化学习仍具挑战性。本文提出了一种强化学习框架，旨在优化传送带速度，同时最大限度减少对控制系统其他部分的干扰。基于真实数据的测试表明，该框架不仅超越了性能要求（99.8%的产品被包装），还维持了质量（100%的填充箱体）。与现有方案相比，所提出的框架提升了生产率，实现了更平滑的控制，并减少了计算时间。