Multi-Task Sensorization of Soft Actuators Using Prior Knowledge
Multi-Task Sensorization of Soft Actuators Using Prior Knowledge
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DOI：
10.1109/icra.2019.8793697
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Vincent Wall;O. Brock
中科院分区：
文献类型：
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作者：
Vincent Wall;O. Brock
软体机器人驱动器所有可能的变形空间极其庞大。无论传感器的数量和类型如何，都不可能明确地测量每个内部自由度。然而，仅使用几个位置合适的传感器来测量与任务相关的变形的一个较小子集是可能的。但是对于不同的任务，软体驱动器的变形行为可能会有显著差异。我们没有为新任务寻找新的传感器放置位置（这会导致每个任务都需要一只单独的手），而是提出了一种方法，该方法保留原始传感器，并利用关于每个任务的先验知识将现有带传感器的驱动器的适用性扩展到新任务。我们通过RBO Hand 2的PneuFlex驱动器的例子来展示我们的方法。当为单个任务给驱动器安装传感器时，传感器模型不能很好地迁移到其他任务。使用我们的多任务方法，我们训练利用任务先验知识的新传感器模型。新模型在不改变传感器硬件的情况下提高了对新任务的测量精度。
The space of all possible deformations of soft robotic actuators is extremely large. It is impossible to explicitly measure each internal degree of freedom, regardless of the number and types of sensors. It is, however, possible to measure a smaller subset of task-relevant deformations using only a few well-placed sensors. But for a different task, the soft actuator’s deformation behavior might differ significantly. Instead of finding a new sensor placement for the new task, which would result in a separate hand for every task, we propose a method that maintains the original sensors and uses prior knowledge about each task to extend the applicability of the existing sensorized actuators to new tasks. We demonstrate our approach by the example of a PneuFlex actuator of the RBO Hand 2. When sensorizing the actuator for a single task, the sensor model does not transfer well to other tasks. Using our multi-task method, we train new sensor models that use prior knowledge about the tasks. The new models improve measurement accuracy for the new tasks without having to change the sensor hardware.