摘要： 机器人以及视觉系统在工厂的大量应用推动了小批量、多品种的混线生产，也使得产品目标尺寸规格的多样化及到达时序的不确定性急剧增加，导致产线大量衔接段存在的堆叠任务仍然极具挑战。随着序列中目标数量增加，堆叠任务的求解时间及解精度难以保障。针对上述问题，提出一种面向序列堆叠任务的刺激记忆混合寻优算法，该算法将序列堆叠任务分解为组合块知识库构建与堆叠决策优化两个子任务。首先，在初始待堆叠目标序列中搜索满足质量阈值的基础目标组合以构建组合块知识库，该过程引入刺激记忆机制来动态更新现有组合知识。其次，将组合块等效处理为一个宏目标后对所有目标的放置顺序及放置姿态进行联合优化。基于不同尺寸分布数据集上的对比实验结果表明，相较于基线算法，所提算法在实现最优堆叠空间平均填充率的情况下至少能够减少 4.94% 的堆叠方案求解时间，验证了其在序列堆叠任务中的有效性。消融实验结果表明，所提完整算法在求解时间上表现最优，验证了该算法结构设计的合理性。

Abstract: The widespread application of robots and vision systems in factories has promoted mixed-line production characterized by small batches and multiple product varieties, while also sharply increasing the diversity of target size specifications and the uncertainty of arrival sequences, thereby making stacking tasks at many transition stages of production lines still highly challenging. With the increase in the number of targets in the sequence, it becomes difficult to guarantee both the solution time and the solution quality of the stacking task. To address the above issues, a hybrid optimization algorithm with stimulus memory for sequence stacking tasks is proposed. The algorithm decomposes the sequence stacking task into two subtasks: combinational block knowledge base construction and stacking decision optimization. First, basic target combinations satisfying quality thresholds are searched for in the initial sequence of targets to be stacked, so as to construct a combinational block knowledge base. During this process, a stimulus memory mechanism is introduced to dynamically update the existing combinational knowledge. Subsequently, each combinational block is equivalently treated as a macro-target, and the placement sequence and placement orientation of all targets are jointly optimized. Comparative experimental results on datasets with different size distributions show that, compared with the baseline algorithms, the proposed algorithm can reduce the solution time of stacking plans by at least 4.94% while achieving the optimal average filling rate of stacking space, which verifies its effectiveness in sequence stacking tasks. The ablation experimental results show that the proposed complete algorithm achieves the best performance in terms of solution time, which validates the rationality of the proposed algorithmic architecture.