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：智能制造正日益重要，这是因为在最大化生产效率和灵活性的同时，最小化浪费和交付周期成为了日益增长的需求。本研究探讨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。此类方案面临的一个主要问题是产品供应不稳定，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通常效果不佳，往往违反行业要求。相比之下，强化学习具备通过基于经验学习响应性和预测性策略来解决这一问题的潜力。然而，在高度复杂的控制方案中应用强化学习仍颇具挑战。本文提出了一种强化学习框架，旨在优化传送带速度的同时，尽量减少对控制系统其他部分的干扰。在实际数据测试中，该框架超越了性能要求（99.8%的包装产品）并保持了质量（100%的满盒）。与现有解决方案相比，我们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。