Analysis of self-adaptive and self-organisational molecular motor units to inform the construction of scalable biomimetic soft-robotic modules.

分析自适应和自组织分子运动单元，为可扩展仿生软机器人模块的构建提供信息。

• 批准号: 2445773
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2445773
• 关键词: Analysis; self; adaptive; organisational; molecular

Self-adaptive and self-organising force inducing fibres are found in human cells, in structures such as the cilia of a respiratory endothelial cell, the flagellum of a spermatozoon or within the sarcomere of muscle cells. These have no neurological input, merely being an arrangement of microtubules within a fibrous sheath, and as such must perform these control mechanisms intrinsically, however the process by which this occurs is largely unknown. This is particularly impressive in the case of the spermatozoon flagellum, which at 0.4-0.5 microns in diameter is the smallest spontaneous force generating system capable of achieving biological function.A deeper knowledge of the mechanisms at play within these cells could have a hugely beneficial impact on the field of robotics if they could be recreated - knowing how much power to apply for a task with unknown force parameters is a computationally complex problem within robotics. Artificial muscle fibres containing an intrinsic feedback mechanism would be able to perform more complex behaviour unsupervised and provide a huge versatility in scale - molecular motors within the body drive motion at the micro scale and can self-organise into far larger macro scale structures.Multiple theories exist on the causes of spontaneous oscillations, organisation and adaptation but are difficult to verify or refute - the aim of this project will be to recreate these mechanisms artificially in a laboratory setting by constructing analogous biomimetic macroscale soft robotics models of the molecular active units in order to analyse and better understand said mechanisms. If an appropriate model is created, the aim will shift toward its utilisation in creating larger scale robots capable of performing simple tasks (such as swimming via travelling waves) with little or no computational input. Focus will be put on making the models non-prescriptive - individual motor units should be either oscillatory or uni-directional, with organisations into more complex movements purely a factor of the architecture of the model. Trying to understand and take advantage of microscopic problems using macroscopic parallels is an under-developed field and this presents an opportunity to bring together molecular dynamics and soft robotics in a truly novel and holistic way. If this project is successful, it could have a broad impact on the field of robotics. Reducing the computational power required for feedback in robots that perform delicate tasks would allow either space to be freed up for other processes, meaning smarter robots, or allowing a reduction in the size of the processing hardware needed, meaning smaller and more efficient robots as some computational processes could be encoded morphologically.This research will be performed in collaboration with 'SoftLab Bristol' at the British Robotics Laboratory.This project falls within the EPSRC 'Engineering' research area, particularly the topics of Control engineering and Robotics.

自适应和自组织力诱导纤维存在于人体细胞中，如呼吸内皮细胞的纤毛、精子的鞭毛或肌肉细胞的肌节中。它们没有神经输入，仅仅是纤维鞘内的微管排列，因此必须内在地执行这些控制机制，然而，其发生的过程在很大程度上是未知的。这在精子鞭毛的例子中尤其令人印象深刻，鞭毛直径在0.4-0.5微米，是能够实现生物功能的最小的自发力产生系统。如果这些细胞能够被重新创造出来，那么对这些细胞中起作用的机制的更深入的了解可能会对机器人领域产生巨大的有益影响——知道在未知力参数的情况下应用多少功率是机器人技术中一个计算复杂的问题。含有内在反馈机制的人造肌肉纤维将能够在无人监督的情况下执行更复杂的行为，并在规模上提供巨大的多功能性——体内的分子马达在微观尺度上驱动运动，并可以自组织成更大的宏观尺度结构。关于自发振荡，组织和适应的原因存在多种理论，但难以验证或反驳-本项目的目的是通过构建分子活性单元的类似仿生宏观软机器人模型，在实验室环境中人工重建这些机制，以便分析和更好地理解所述机制。如果建立了一个合适的模型，目标将转向利用它来制造更大规模的机器人，这些机器人能够在很少或没有计算输入的情况下执行简单的任务（比如在行波中游泳）。重点将放在使模型非规定性上——单个运动单元应该是振荡的或单向的，而组织成更复杂的运动纯粹是模型结构的一个因素。试图用宏观的类比来理解和利用微观问题是一个欠发达的领域，这提供了一个机会，将分子动力学和软机器人技术以一种真正新颖和整体的方式结合在一起。如果这个项目成功，它可能会对机器人领域产生广泛的影响。在执行精细任务的机器人中，减少反馈所需的计算能力将允许释放空间用于其他过程，这意味着更智能的机器人，或者允许减少所需的处理硬件的尺寸，这意味着更小、更高效的机器人，因为一些计算过程可以被形态学地编码。这项研究将与英国机器人实验室的“布里斯托尔软实验室”合作进行。该项目属于EPSRC“工程”研究领域，特别是控制工程和机器人的主题。