As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordinating diverse robots for collaborative assembly. To this end, we propose a Stackelberg game-theoretic learning approach. By leveraging Stackelberg games, we characterize robot collaboration through leader-follower interaction to enhance strategy seeking and ensure task completion. To enhance applicability across tasks, we introduce a novel multi-agent learning algorithm: Stackelberg double deep Q-learning, which facilitates automated assembly strategy seeking and multi-robot coordination. Our approach is validated through simulated assembly tasks. Comparison with three alternative multi-agent learning methods shows that our approach achieves the shortest task completion time for tasks. Furthermore, our approach exhibits robustness against both accidental and deliberate environmental perturbations.

翻译：随着装配任务复杂度的增加，多机器人协作对于完成任务变得至关重要。然而，集中式任务规划已难以适应机器人日益提升的智能性与多功能性，以及日益增长的定制化订单需求。目前亟需一种高效自动化的规划机制，能够协调异构机器人完成协作装配。为此，我们提出了一种基于斯塔克尔伯格博弈理论的学习方法。通过利用斯塔克尔伯格博弈，我们以领导者-跟随者交互模式刻画机器人协作关系，从而增强策略搜索能力并确保任务完成。为提升该方法在不同任务间的适用性，我们引入了一种新型多智能体学习算法——斯塔克尔伯格双深度Q学习，该算法能够实现自动化装配策略搜索与多机器人协调。通过仿真装配任务验证了所提方法的有效性。与三种替代性多智能体学习方法的比较表明，我们的方法在任务完成时间上达到最短。此外，该方法对意外及人为环境扰动均表现出良好的鲁棒性。