Tariverdi Abbas, Venkiteswaran Venkatasubramanian Kalpathy, Richter Michiel, Elle Ole J, Tørresen Jim, Mathiassen Kim, Misra Sarthak, Martinsen Ørjan G
Department of Physics, University of Oslo, Oslo, Norway.
Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands.
Front Robot AI. 2021 Mar 18;8:631303. doi: 10.3389/frobt.2021.631303. eCollection 2021.
This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.
本文介绍并验证了一种基于神经网络方法的软连续体机器人实时动态预测模型。所提出的模型使用基于神经网络的策略和连续体力学原理提供了一个实时预测框架。采用时空积分方案对连续动力学进行离散化，并将软连续体机器人每个节点的平移和旋转动力学方程解耦。然后，利用所得架构开发基于递归神经网络的分布式预测算法。所提出的基于递归神经网络的并行预测方案不依赖于计算量大的算法；因此，它在实时应用中很有用。此外，通过仿真展示了该方法在软连续弹性体上的性能，并通过对磁驱动软连续体机器人的实验对该方法进行了验证。结果表明，所提出的模型优于经典建模方法，如柯塞尔杆模型，同时也显示了在实际应用中的可能性。
