RI: Small: Collaborative Research: Evolutionary Approach to Optimal Morphology and Control of Transformable Soft Robots

RI：小型：协作研究：可变形软机器人的最佳形态和控制的进化方法

• 批准号: 2006616
• 负责人: Kevin Galloway
• 金额: $ 4万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006616&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Evolutionary

The overall objective of the project is to leverage the principles of evolution in order to develop a new type of complex soft robots (robots that are constructed from compliant material) that can change their shape and mechanical properties to assume different forms in order to overcome environmental challenges such as uneven terrain. The motivation comes from the ways animals achieve efficient locomotion in their habitats by varying body shape and mechanical properties as a result of evolution over millions of years. Based on prior numerical results, the investigators hypothesize that highly adaptable locomotion can be achieved when given enough complexity in terms of shape deformation and mechanical properties. Current bioinspired soft robots, however, have limited capability to investigate such hypothesis. One key goal in this proposal is to develop innovative soft robotic platforms to study, for the first time, these numerical findings. The planned research has the potential to significantly enhance the functionality, adaptability, and versality of high-dimensional soft robots. The broader impact would be significant in several areas where soft robots have shown promise, particularly in inspection operations and search-and-rescue missions. The project is also expected to have a significant educational impact by engaging undergraduate students in the planned research activities and developing course content and new courses geared towards soft robots. Outreach efforts focus on workshops, public lectures, and engagements with elementary, middle, and high schools to foster collaborations with the local communities. The planned research is poised to unlock the evolutionary secrets of biological systems and open the door for the next generation of energy-efficient, adaptable, versatile, and human-friendly robots. To meet these goals, investigators propose to develop novel transformable and stiffness controllable soft robots and apply evolutionary computing to generate optimal morphological and stiffness trajectories for locomotion. The investigators employ evolutionary computing-based learning models to circumvent the curse of dimensionality associated with complex soft robotic systems and develop a comprehensive framework for developing optimal and robust control schemes and testing thereof using a series of rigorous experimental evaluations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的总体目标是利用进化原理开发一种新型的复杂软机器人（由柔性材料制成的机器人），这种机器人可以改变形状和机械性能，以采取不同的形式，以克服不平坦地形等环境挑战。 其动机来自于动物在其栖息地中通过数百万年的进化改变身体形状和机械特性来实现有效运动的方式。 基于先前的数值结果，研究人员假设，当形状变形和机械特性具有足够的复杂性时，可以实现高度适应性的运动。 然而，目前的仿生软机器人对这种假设的研究能力有限。该提案的一个关键目标是开发创新的软机器人平台，以首次研究这些数值结果。计划中的研究有可能显着提高高维软机器人的功能性，适应性和多样性。更广泛的影响将在软机器人显示出希望的几个领域产生重大影响，特别是在检查行动和搜索和救援任务中。该项目还有望通过让本科生参与计划的研究活动，开发面向软机器人的课程内容和新课程，产生重大的教育影响。外展工作的重点是研讨会，公开讲座，并与小学，初中和高中的参与，以促进与当地社区的合作。这项计划中的研究有望解开生物系统的进化秘密，并为下一代节能、适应性强、多功能和对人类友好的机器人打开大门。为了实现这些目标，研究人员建议开发新的可变形和刚度可控的软机器人，并应用进化计算来生成最佳的形态和刚度轨迹的运动。研究人员使用进化计算-基于学习模型，以规避与复杂软机器人系统相关的维度诅咒，并开发一个全面的框架，用于开发最佳和鲁棒的控制方案并使用一系列严格的实验评估对其进行测试。该奖项反映了NSF的法定使命，并通过利用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。