Robust Multimodal Indirect Sensing for Soft Robots Via Neural Network-Aided Filter-Based Estimation.
Robust Multimodal Indirect Sensing for Soft Robots Via Neural Network-Aided Filter-Based Estimation.
复制标题
通过神经网络辅助基于滤波器的估计实现软体机器人的鲁棒多模态间接传感。
DOI：
10.1089/soro.2020.0024
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发表时间：
2021
期刊：
影响因子：
7.9
通讯作者：
Chee Pin Tan
中科院分区：
文献类型：
--
作者：
J. Loo;Z. Y. Ding;Vishnu Monn Baskaran;S. Nurzaman;Chee Pin Tan
感官数据对于软机器人感知至关重要。然而，由于其固有的柔软性，将传感器集成到软机器人仍然具有挑战性。另一种方法是通过估计方案进行间接传感，该方案使用机器人动力学和可用的测量来估计传感器测量的变量。然而，为软机器人开发一个足够有效的估计方案并不容易。首先，需要一个数学模型；由于其复杂的动力学，软机器人的建模对分析的要求很高。其次，它应该使用最少的传感器对内部和外部变量执行多模式传感，最后，它必须对传感器故障具有鲁棒性。在本文中，我们提出了一种基于循环神经网络的自适应无味卡尔曼滤波器（RNN-AUKF）架构来估计基于气动的软手指的本体感受状态和外感受未知输入。为了解决软机器人建模的挑战，我们采用了使用 RNN 的数据驱动方法。然后，我们将 AUKF 与未知输入估计器互连，以使用单个嵌入式柔性传感器执行多模态传感。我们还从数学上证明，估计误差对于传感器退化（噪声和漂移）是有限的。实验结果表明，与基准方法相比，RNN-AUKF 在准确性和鲁棒性方面取得了更好的整体性能。所提出的方案还扩展到多指软夹具，并且对于分布外的传感器动力学具有鲁棒性。这项研究的成果在软机器人中实现鲁棒的多模态间接传感方面具有巨大的潜力。
Sensory data are critical for soft robot perception. However, integrating sensors to soft robots remains challenging due to their inherent softness. An alternative approach is indirect sensing through an estimation scheme, which uses robot dynamics and available measurements to estimate variables that would have been measured by sensors. Nevertheless, developing an adequately effective estimation scheme for soft robots is not straightforward. First, it requires a mathematical model; modeling of soft robots is analytically demanding due to their complex dynamics. Second, it should perform multimodal sensing for both internal and external variables, with minimal sensors, and finally, it must be robust against sensor faults. In this article, we propose a recurrent neural network-based adaptive unscented Kalman filter (RNN-AUKF) architecture to estimate the proprioceptive state and exteroceptive unknown input of a pneumatic-based soft finger. To address the challenge in modeling soft robots, we adopt a data-driven approach using RNNs. Then, we interconnect the AUKF with an unknown input estimator to perform multimodal sensing using a single embedded flex sensor. We also prove mathematically that the estimation error is bounded with respect to sensor degradation (noise and drift). Experimental results show that the RNN-AUKF achieves a better overall performance in terms of accuracy and robustness against the benchmark method. The proposed scheme is also extended to a multifinger soft gripper and is robust against out-of-distribution sensor dynamics. The outcomes of this research have immense potentials in realizing a robust multimodal indirect sensing in soft robots.