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.

中文翻译：

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基于神经网络辅助滤波器估计的软机器人鲁棒多模态间接感知

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中仍然具有挑战性。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测量值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简单。首先，它需要一个数学模型；由于其复杂的动力学，软机器人的建模在分析上要求很高。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。为了解决对软机器人建模的挑战，我们采用了一种使用 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式传感。我们还在数学上证明了估计误差与传感器退化（噪声和漂移）有关。实验结果表明，RNN-AUKF 相对于基准方法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持器，并且对分布外传感器动力学具有鲁棒性。