Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges traditional static assumptions. The DBPF endows the robot with the reactivity to pick multiple moving arbitrary objects while avoiding dynamic obstacles, such as the moving bin. Combined with scene-level pose generation, the proposed pose selection metric leverages the Tendency-Aware Manipulability Network optimizing suction pose determination. Heuristic task-specific designs like velocity-matching, dynamic obstacle avoidance, and the resight policy, enhance the picking success rate and reliability. Empirical experiments demonstrate the importance of these components. Our method achieves an average 84% success rate, surpassing the 60% of the most comparable baseline, crucially, with zero collisions. Further evaluations under diverse dynamic scenarios showcase DBPF's robust performance in dynamic bin-picking. Results suggest that our framework offers a promising solution for efficient and reliable robotic bin-picking under dynamics.

翻译：效率和可靠性是机器人料箱抓取的关键，直接影响自动化工业流程的生产力。然而，传统方法要求静态物体和固定碰撞，导致部署受限、运行效率低下及流程不可靠。本文提出一种动态料箱抓取框架（DBPF），挑战了传统的静态假设。DBPF赋予机器人对多个移动随机物体的反应能力，同时避免动态障碍物（如移动料箱）的干扰。结合场景级姿态生成，所提出的姿态选取度量利用倾向感知可操作性网络优化吸盘姿态决策。启发式任务特定设计（如速度匹配、动态避障及重新观测策略）提升了抓取成功率和可靠性。实证实验证明了这些组件的重要性。我们的方法实现了平均84%的成功率，超越最可比基线的60%，且关键的是实现了零碰撞。在多种动态场景下的进一步评估展示了DBPF在动态料箱抓取中的鲁棒性能。结果表明，该框架为动态环境下高效可靠的机器人料箱抓取提供了一种有前景的解决方案。