Examining the interface of neuroscience and robotics, developing methods for control and tactile feedback in prosthetic hands

检查神经科学和机器人技术的接口，开发假手的控制和触觉反馈方法

• 批准号: 2284221
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284221
• 关键词: Examining; interface; neuroscience; robotics; developing

My research combines developments in two difference fields. In computational neuroscience and computer science, Spiking Neural Networks (SNN) are heralded as the third generation of neural networks due to their utilization of the temporal domain in a feedforward structure. Coupled with a significant reduction in required computational resources relative to previous network structures, they offer a new technique to explore our world. In the field of tactile robotics, the research team here at Bristol have pioneered a 3D printed finger-like structure known as the TacTip. The silicone mould depresses upon contact with a surface and current deep learning techniques are utilised in the lab to detect surface texture.In a pursuit to explore the feasibility of human robot interaction via prosthesis, my research aims to incorporate the low-computational power of SNN with a scaled-down version of the TacTip on a prosthetic hand. To begin, I am developing a generalizable unsupervised SNN and investigating the success of the network in detecting a unique finger motions such as tapping, edge-detection and slip. Some ideas for future work throughout my PhD progression involve haptic feedback for the user and incorporation across multi-finger TacTips.

我的研究结合了两个不同领域的发展。在计算神经科学和计算机科学中，脉冲神经网络（SNN）被誉为第三代神经网络，因为它在前馈结构中利用了时域。与以前的网络结构相比，它们大大减少了所需的计算资源，为探索我们的世界提供了一种新技术。在触觉机器人领域，布里斯托尔大学的研究团队开创了一种3D打印的类似手指的结构，称为“触觉”。硅胶模具在与表面接触时压下，目前在实验室中使用深度学习技术来检测表面纹理。为了探索通过假肢进行人机交互的可行性，我的研究旨在将SNN的低计算能力与按比例缩小的tactical版本结合在假肢上。首先，我正在开发一个可推广的无监督SNN，并研究该网络在检测独特手指动作（如敲击、边缘检测和滑动）方面的成功。在我的博士研究过程中，我对未来工作的一些想法涉及到用户的触觉反馈和跨多指触觉的结合。