Adaptive Tracking Control of Soft Robots Using Integrated Sensing Skins and Recurrent Neural Networks
Adaptive Tracking Control of Soft Robots Using Integrated Sensing Skins and Recurrent Neural Networks
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DOI：
10.1109/icra48506.2021.9561447
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Lasitha Weerakoon;Z. Ye;Rahul S. Bama;E. Smela;Miao Yu;N. Chopra
中科院分区：
文献类型：
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作者：
Lasitha Weerakoon;Z. Ye;Rahul S. Bama;E. Smela;Miao Yu;N. Chopra
本文研究了软机器人的综合估计与控制问题。部署闭环控制器的一个重大挑战是通过软机器人中的集成传感实现可靠的本体感受。尽管在软机器人的制造、建模和基于模型的控制方面取得了相当大的进展，但集成传感和估计仍处于起步阶段。为此，本文介绍了一种新的方法来估计的程度的弯曲的软机器人使用可拉伸的传感皮肤。皮肤是乳胶膜上喷涂的压阻传感层。通过使用递归神经网络估计从应变信号到曲率程度的映射。我们研究了单节柔性机器人的单向弯曲和双向弯曲。此外，一个自适应控制器的开发，以跟踪弯曲度的软机器人在动态不确定性的存在。随后，使用集成的软测量皮肤，我们实验证明成功的曲率跟踪控制的软机器人。
In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sensing in soft robots. Despite the considerable advances accomplished in fabrication, modelling, and model-based control of soft robots, integrated sensing and estimation is still in its infancy. To that end, this paper introduces a new method of estimating the degree of curvature of a soft robot using a stretchable sensing skin. The skin is a spray-coated piezoresistive sensing layer on a latex membrane. The mapping from the strain signal to the degree of curvature is estimated by using a recurrent neural network. We investigate uni-directional bending as well as bi-directional bending of a single-segment soft robot. Moreover, an adaptive controller is developed to track the degree of curvature of the soft robot in the presence of dynamic uncertainties. Subsequently, using the integrated soft sensing skin, we experimentally demonstrate successful curvature tracking control of the soft robot.