Manipulating deformable objects by interleaving prediction, planning, and control
Manipulating deformable objects by interleaving prediction, planning, and control
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DOI：
10.1177/0278364920918299
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发表时间：
2020-06-19
影响因子：
9.2
通讯作者：
Berenson, Dmitry
中科院分区：
文献类型：
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作者：
McConachie, Dale;Dobson, Andrew;Berenson, Dmitry
我们提出了一个用于可变形物体操作的框架，该框架将规划和控制交织在一起，能够在不依赖高保真建模或模拟的情况下实现复杂的操作任务。我们要解决的关键问题是，我们应该何时使用规划，何时使用控制来完成任务？规划器旨在通过复杂的构型空间找到路径，但对于高度欠驱动系统，比如可变形物体，即使有高保真模型，要实现特定构型也非常困难。相反，控制器可以被设计用来实现特定构型，但由于障碍物，它们可能会陷入不理想的局部最小值。我们的方法由三个部分组成：（1）一个全局运动规划器，用于生成可变形物体的大致运动；（2）一个局部控制器，用于细化可变形物体的构型；（3）一种新颖的死锁预测算法，用于确定何时使用规划以及何时使用控制。通过将规划与控制分离，我们能够使用可变形物体的不同表示形式，降低整体复杂性并实现高效的运动计算。我们为我们的规划器提供了概率完备性的详细证明，尽管我们的系统是欠驱动的且没有转向函数，但该证明仍然有效。然后我们证明，我们的框架能够在模拟中成功地用绳子和布料执行几个操作任务，这些任务单独使用我们的控制器或规划器是无法完成的。这些实验表明，我们的规划器能够高效地生成路径，在四个场景中的三个场景里，平均不到一秒就能找到一条可行路径。我们还表明，我们的框架在一个具有16个自由度的物理机器人上是有效的，在该机器人上，可达性和双臂约束使得规划更加困难。
We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.