Self-organising multiple human–robot collaboration: A temporal subgraph reasoning-based method

Multiple Human–Robot Collaboration (HRC) requires self-organising task allocation to adapt to varying operation goals and workspace changes. However, nowadays an HRC system relies on predefined task arrangements for human and robot agents, which fails to accomplish complicated manufacturing tasks consisting of various operation sequences and different mechanical parts. To overcome the bottleneck, this paper proposes a temporal subgraph reasoning-based method for self-organising HRC task planning between multiple agents. Firstly, a tri-layer Knowledge Graph (KG) is defined to depict task-agent-operation relations in HRC tasks. Then, a subgraph mechanism is introduced to learn node embeddings from subregions of the HRC KG, which distills implicit information from local object sets. Thirdly, a temporal reasoning module is leveraged to integrate features from previous records and update the HRC KG for forecasting humans’ and robots’ subsequent operations. Finally, a car engine assembly task is demonstrated to evaluate the effectiveness of the proposed method, which outperforms other benchmarks in experimental results.