Spatiotemporal path tracking via deep reinforcement learning of robot for manufacturing internal logistics

The development of engineering technology, the logistics system composed of wheeled mobile robots (WMR) and automated guided vehicles (AGV) have been widely used in industrial scenes. However, traditional manufacturing scenes with dense layout, such as weaving workshops, have higher requirements for automatic transportation of materials. How to realize the intelligent transportation and management of materials in the manufacturing workshop and balance the efficiency and safety of material processing and transportation is still a great challenge. To address this problem, we designed a logistics system based on cloth-roll handling robot (CHR) and its path tracking hybrid deep reinforcement learning (DRL) considering spatiotemporal efficiency and safety in weaving workshop. This research first focuses on the design of a dynamic observation Markov decision-making process that integrates scene features. Further, a deep reinforcement learning considering the heterogeneity of observation data is proposed to obtain the optimal action solution. Then, a distributed scenarios training is implemented to improve the interaction ability between agents and the environment in complex scenes. In addition, the balance between dynamic observation and on-site calculation is considered in the path tracking for actual weaving workshop.