Learning Multi-agent Multi-machine Tending by Mobile Robots
This work addresses the worker shortage in manufacturing by enabling flexible and scalable automation, though it is incremental as it builds on existing MARL techniques.
The paper tackles the problem of machine tending in manufacturing using mobile robots, introducing a multi-agent reinforcement learning framework with an attention-based encoding mechanism that outperforms baseline methods in task success, safety, and resource utilization.
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.