Collaborative Optimization of Service Scheduling for Industrial Cloud Robotics Based on Knowledge Sharing

Abstract Industrial Cloud Robotics (ICR), which has the characteristics of resource sharing, convenient access and high efficiency, is the combination of Cloud Computing and Industrial Robots. In the current manufacturing workshop, most industrial robots that are not connected to each other use onboard processors and memories with limited resources, which leading to the constraints of multi-robot information sharing. However, knowledge sharing for multi-robot collaborative optimization is very important. In the service scheduling optimization of industrial robots oriented to workshop manufacturing tasks, the lack of knowledge sharing seriously restricts the further performance improvement of the collaborative optimization. Aiming at this problem, the collaborative optimization framework of service scheduling for industrial cloud robotics is built, and then a cloud-based knowledge sharing mechanism for industrial robots and a collaborative optimization method of service scheduling based on Deep Reinforcement Learning (DRL) are proposed, so as to realize a comprehensive performance improvement of the whole manufacturing system. Finally, a case study is implemented to verify the effectiveness of the proposed method.