Optimized Multi-Robot Task Allocation for Efficient Raw Material Supply in Smart Factories

In Industry 4.0, smart factories are essential to next-generation manufacturing, driven by advanced technologies like Cyber-Physical Systems (CPS) and the Internet of Things (IoT), rely on efficient material handling systems such as automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for flexible. However, current multi-robot systems face scalability and efficiency problems in raw material supply. This paper introduces a multi-robot task allocation approach to optimize raw material supply in smart factory supply chains. A task optimization model is proposed, integrating task requirements and travel distances, reinterpreting the supply challenge as a multi-robot task allocation problem. To enhance efficiency and reduce energy consumption, a collaborative allocation method is used, combining greedy task distribution with Nash equilibrium principles. Experimental results confirm that the proposed algorithm effectively manages supply tasks, and has the potential to lighten the energy burden in smart factories.