Research on Multi cabin Collaborative Assembly Method Based on Multi Agent Reinforcement Learning

Abstract

Abstract In the domain of multi-cabin assembly, the prevailing method entails step-by-step docking assembly, which often leads to the accumulation of errors throughout the assembly process. This paper employs multi-agent reinforcement learning techniques to facilitate collaborative assembly of the lateral force device assembly, thereby enhancing assembly quality. Initially, the assembly process of the final assembly is scrutinized to establish a conducive learning environment. Subsequently, a novel agent configuration is proposed, wherein two agents are deployed to represent a single motion unit. Following this, a diverse range of algorithms is employed to train the model, enabling the selection of the most appropriate algorithm and optimization of pertinent parameters. The resultant multi-agent assembly paths are then generated for comprehensive analysis and comparison. Finally, simulation verification of the paths is conducted, accompanied by an analysis of the assembly-induced stress during the assembly process.