A Two-Stage MLP-LSTM Network-Based Task Planning Method for Human–Robot Collaborative Assembly Scenarios

In many current assembly scenarios, efficient collaboration between humans and robots can improve collaborative efficiency and quality. However, the efficient arrangement of human–robot collaborative (HRC) tasks constitutes a significant challenge. In a collaborative workspace where humans and robots collaborate on assembling a shared product, the determination of task allocation between them is of crucial importance. To address this issue, offline feasible HRC paths are established based on assembly task constraint information. Subsequently, the HRC process is simulated within a virtual environment leveraging these feasible paths. Human assembly intentions are explicitly expressed through human assembly trajectories, and implicitly expressed through simulation results such as assembly time and human–robot resource allocation. Furthermore, a two-stage MLP-LSTM network is employed to train and optimize the assembly simulation database. In the first stage, a sequence generation model is trained using high-quality HRC processes. Then, the network learns human evaluation patterns to score the generated sequences. Ultimately, task allocation for HRC is performed based on the high-scoring generated sequences. The effectiveness of the proposed method is demonstrated through assembly scenarios of two products. Compared with traditional optimization methods like DFS and Greedy, the human collaboration ratio has been optimized by 10%, while the collaborative quality evaluation has been improved by 3%.