Shared mental models-based collaboration method in assembly tasks for multi-agent self-organizing systems

Multi-agent Self-organizing Systems (MASOSs) feature scalability, flexibility, and robustness, and their application in complex assembly tasks has garnered increasing attention. However, a pressing challenge persists in effectively curbing potential negative outcomes in MASOSs while guiding them towards positive ones. To address this challenge, in this paper the concept of Shared Mental Models (SMMs) is applied to MASOSs, and an SMMs-based collaboration method in assembly tasks for MASOSs is proposed. Integrating individual mental models from multiple agents, an SMMs structure for MASOSs is initially constructed. Building upon this structure, a collaboration method for MASOSs to execute assembly tasks is afterward proposed, and the impact of SMMs on task performance and emergent behaviors of MASOSs is explored through an “L-shape” assembly case. The results demonstrate that the SMMs-based method significantly improves task reliability, task efficiency, time efficiency, and energy efficiency. Additionally, we find that increasing agent team size initially leads to positive emergent behaviors due to scale advantages, but surpassing the optimal size results in negative emergent behaviors due to coordination disadvantages. Furthermore, the degree of knowledge sharing among agents also has a significant impact on task performance and emergent behaviors. SMMs are expected to serve as a mechanism to regulate and optimize team emergent behaviors, ultimately achieving optimal system performance.