用神经网络实现软臂形变快速平滑重建，速度提升10万倍。
A Neural Network-based Framework for Fast and Smooth Posture Reconstruction of a Soft Continuum Arm
- 输入离散位姿测量，通过神经网络预测应变分布
- 相比传统方法快5个数量级，精度相当
- 适用于软体机械臂实时控制与仿真
本文提出一种基于神经网络的框架，用于从沿软连续臂（SCA）有限位置获取的噪声位姿测量中估计其形状。神经网络输入这些测量值，输出应变的有限维近似，再用于重构无限维的平滑姿态。该问题对多种软体机器人应用至关重要，但因柔性特性导致姿态和应变的无限维重建困难，以往方法计算成本高。所提快速平滑重建方法在保持相近精度的前提下，比过去方案快五数量级。框架在两个测试平台验证：模拟八爪鱼肌肉臂和真实的BR2气动软机械臂。
原文摘要 · Abstract (English)
A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the pose at a finite number of locations along the length of the arm. The neural network takes as input these measurements and produces as output a finite-dimensional approximation of the strain, which is further used to reconstruct the infinite-dimensional smooth posture. This problem is important for various soft robotic applications. It is challenging due to the flexible aspects that lead to the infinite-dimensional reconstruction problem for the continuous posture and strains. Because of this, past solutions to this problem are computationally intensive. The proposed fast smooth reconstruction method is shown to be five orders of magnitude faster while having comparable accuracy. The framework is evaluated on two testbeds: a simulated octopus muscular arm and a physical BR2 pneumatic soft manipulator.
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