提出分层算法解决人机协同生产中的动态任务分配难题。
A hierarchical spatial-aware algorithm with efficient reinforcement learning for human-robot task planning and allocation in production
- 分层设计高阶规划与低阶分配，结合强化学习与空间感知。
- 在3D模拟中实现高效任务分配，显著提升复杂环境下的响应速度。
- 适合智能制造、工业自动化领域研究者参考。
在先进制造系统中，人机协同完成生产流程。有效的任务规划与分配（TPA）对提高生产效率至关重要，但在复杂动态环境中仍具挑战性。人类与机器人的动态特性，特别是需考虑空间信息（如人员实时位置及移动距离），使TPA更加复杂。为此，我们将生产任务分解为可管理的子任务，并提出一种实时分层人机TPA算法，包含高层任务规划与底层任务分配两个代理。高层代理采用高效的基于缓冲区的深度Q学习方法（EBQ），减少训练时间，增强在长期稀疏奖励问题中的性能；底层代理则设计基于路径规划的空间感知方法（SAP），将任务分配给合适的人机资源，实现相应顺序子任务。我们在3D仿真环境中对复杂的实时生产过程进行了实验。结果表明，所提出的EBQ&SAP方法能有效解决复杂动态生产环境中的人机TPA问题。
原文摘要 · Abstract (English)
In advanced manufacturing systems, humans and robots collaborate to conduct the production process. Effective task planning and allocation (TPA) is crucial for achieving high production efficiency, yet it remains challenging in complex and dynamic manufacturing environments. The dynamic nature of humans and robots, particularly the need to consider spatial information (e.g., humans' real-time position and the distance they need to move to complete a task), substantially complicates TPA. To address the above challenges, we decompose production tasks into manageable subtasks. We then implement a real-time hierarchical human-robot TPA algorithm, including a high-level agent for task planning and a low-level agent for task allocation. For the high-level agent, we propose an efficient buffer-based deep Q-learning method (EBQ), which reduces training time and enhances performance in production problems with long-term and sparse reward challenges. For the low-level agent, a path planning-based spatially aware method (SAP) is designed to allocate tasks to the appropriate human-robot resources, thereby achieving the corresponding sequential subtasks. We conducted experiments on a complex real-time production process in a 3D simulator. The results demonstrate that our proposed EBQ&SAP method effectively addresses human-robot TPA problems in complex and dynamic production processes.
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