Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect possible grasps for the objects, decide on the object to remove and consequently plan and execute a feasible trajectory to retrieve the chosen object. Over the last years significant progress has been made towards solving these problems. However, when multiple robot arms are cooperating the decision and planning problems become exponentially harder. We propose an integrated multi-arm bin-picking pipeline (IMAPIP), and demonstrate that it is able to reliably pick objects from a bin in real-time using multiple robot arms. IMAPIP solves the multi-arm bin-picking task first at high-level using a geometry-aware policy integrated in a combined task and motion planning framework. We then plan motions consistent with this policy using the BIT* algorithm on the motion planning level. We show that this integrated solution enables robot arm cooperation. In our experiments, we show the proposed geometry-aware policy outperforms a baseline by increasing bin-picking time by 28\% using two robot arms. The policy is robust to changes in the position of the bin and number of objects. We also show that IMAPIP to successfully scale up to four robot arms working in close proximity.
翻译:自动选取文件夹是完全自动化制造和仓库的先决条件。 成功从一个没有结构的垃圾桶中挑选一个物品, 机器人需要首先从一个没有结构的垃圾桶中挑选一个物品, 才能首先检测到物体的可能的切入点, 决定一个要移除的对象, 并随后规划和执行一个可行的轨道以检索选定的对象。 在过去几年中, 解决这些问题已经取得了显著的进展。 但是, 当多个机器人武器正在合作作出决定和规划问题时, 自动挑选是完全自动化的。 我们提议了一个综合的多武器抽取管道( IMAPIP ), 并证明它能够使用多个机器人武器实时从一个垃圾桶中可靠地提取物体。 IMAPIP 首先在高级别上, 使用一个组合任务和动作规划框架中整合的几何自觉化政策, 并随后在解决这些问题上取得了显著的进展。 我们证明这一综合解决方案能够使机器人的手臂合作变得更加强大。 我们在实验中展示了拟议中的几何测地测量政策超越了基线,, 使用两个机器人武器, 将自动打印时间增加 28 。 。 IP 政策非常坚固,, 能够使四个机器人 的物体在近距离上移动 。