Analysing the Motion of Biological Swimmers

分析生物游泳者的运动

• 批准号: EP/S01540X/1
• 负责人: David Hogg
• 金额: $ 31.45万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS01540X%2F1
• 关键词: Analysing; Motion; Biological; Swimmers

Consider a deformable object moving in some medium. In general, one cannot infer the internal deformation of the body based on surface shape alone: it requires detailed knowledge of the mechanical properties and forces operating at every point of the object. And yet, such inverse problems are of considerable practical and fundamental interest in soft matter physics, material science and engineering. We propose that the relatively simple and constrained geometry of the worm C. elegans, and our extensive knowledge of its anatomy and material properties makes the inverse problem tractable. A new and promising approach to the solution of inverse problems is provided by deep learning, which has already transformed performance on standard tasks from across artificial intelligence, particularly in the areas of language and vision, and increasingly now in robotics. The proposed research is to explore the feasibility of using deep learning to understand the internal and external forces acting on the body of C. elegans from video footage of the worm swimming in three dimensional complex fluids.

考虑一个可变形物体在某种介质中运动。一般来说，人们不能仅仅根据表面形状来推断物体的内部变形：它需要详细了解物体每一点的力学性质和作用力。然而，这样的反问题是相当实际的和基本的兴趣在软物质物理，材料科学和工程。我们建议，相对简单和约束的几何形状的蠕虫C。elegans，我们对其解剖学和材料特性的广泛了解使得逆问题易于处理。深度学习提供了一种新的、有前途的解决逆问题的方法，它已经改变了人工智能标准任务的性能，特别是在语言和视觉领域，现在越来越多地应用于机器人领域。拟议的研究是探索使用深度学习来理解作用于C身体的内部和外部力量的可行性。从视频片段中的蠕虫游泳在三维复杂的流体。

项目成果

A monolithic optimal control method for displacement tracking of Cosserat rod with application to reconstruction of C. elegans locomotion

• DOI: 10.1007/s00466-022-02247-x
• 发表时间: 2022-11
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Yongxing Wang;T. Ranner;Thomas P. Ilett;Yan Xia;N. Cohen

Markerless 3D spatio-temporal reconstruction of microscopic swimmers from video

根据视频对微观游泳者进行无标记 3D 时空重建

• 发表时间: 2020
• 作者: Felix Salfelder