Toward Fast and Optimal Robotic Pick-and-Place on a Moving Conveyor
Toward Fast and Optimal Robotic Pick-and-Place on a Moving Conveyor
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DOI：
10.1109/lra.2019.2961605
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发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Yu, Jingjin
中科院分区：
文献类型：
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作者：
Han, Shuai D.;Feng, Si Wei;Yu, Jingjin
机器人拾取（PNP）运输运营机的运输运营可找到广泛的工业应用。实际上，应用了简单的贪婪启发式方法（例如，基于处理单个对象的时间的优先级），以实现合理的效率。我们分析表明，在简化的望远镜机器人模型下，这些贪婪的方法无法确保PNP操作的时间最佳性。为了解决经典解决方案的缺点，我们开发了为预定的有限层计算最佳对象选择序列的算法。我们的方法采用动态的编程技术和其他启发式方法，扩展到数十个对象。特别是，我们开发的快速算法随着运行时间的保证提供，使其适用于需要高通量的实时PNP应用程序。对现实世界应用中使用的主要工业PNP机器人的算法解决方案的广泛评估，即Delta机器人和选择性合规性装配机器人机器人（Scara）机器人，表明，典型的效率增长了约10％-40％的贪婪方法，可以可以实现。
Robotic pick-and-place (PnP) operations on moving conveyors find a wide range of industrial applications. In practice, simple greedy heuristics (e.g., prioritization based on the time to process a single object) are applied that achieve reasonable efficiency. We show analytically that, under a simplified telescoping robot model, these greedy approaches do not ensure time optimality of PnP operations. To address the shortcomings of classical solutions, we develop algorithms that compute optimal object picking sequences for a predetermined finite horizon. Employing dynamic programming techniques and additional heuristics, our methods scale to up to tens to hundreds of objects. In particular, the fast algorithms we develop come with running time guarantees, making them suitable for real-time PnP applications demanding high throughput. Extensive evaluation of our algorithmic solution over dominant industrial PnP robots used in real-world applications, i.e., Delta robots and Selective Compliance Assembly Robot Arm (SCARA) robots, shows that a typical efficiency gain of around 10%-40% over greedy approaches can be realized.