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

RI：小型：协作研究：可变形软机器人最佳形态的进化方法

• 批准号: 2008413
• 负责人: Nicholas Cheney
• 金额: $ 22.9万
• 依托单位: University of Vermont & State Agricultural College
• 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=2008413&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 project is jointly funded by the Robust Intelligence (RI) and the Established Program to Stimulate Competitive Research (EPSCoR).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的原理来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它们的形状和机械性能，以采取不同的形式，以克服诸如不平坦地形等环境挑战。这种动机来自于动物在数百万年的进化过程中，通过改变身体形状和机械特性，在栖息地中实现高效运动的方式。基于先前的数值结果，研究人员假设，当给定足够的形状变形和力学性能复杂性时，可以实现高度自适应的运动。然而，目前的仿生软体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，首次研究这些数值结果。计划中的研究具有显著增强高维软体机器人的功能性、适应性和通用性的潜力。在软机器人已经显示出前景的几个领域，尤其是在检查行动和搜救任务方面，其更广泛的影响将是显著的。该项目还有望通过吸引本科生参与计划的研究活动，开发针对软机器人的课程内容和新课程，对教育产生重大影响。外联工作的重点是研讨会、公开演讲以及与小学、初中和高中的接触，以促进与当地社区的合作。这项计划中的研究将揭开生物系统的进化秘密，并为下一代节能、适应性强、多用途、对人类友好的机器人打开大门。为了实现这些目标，研究人员提出开发新型可变形和刚度可控的软机器人，并应用进化计算来生成运动的最佳形态和刚度轨迹。研究人员采用基于进化计算的学习模型来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，用于开发最优和鲁棒控制方案，并使用一系列严格的实验评估对其进行测试。该项目由健全情报（RI）和建立计划刺激竞争研究（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots

模块化控制器促进软机器人形态和控制的协同优化

• DOI: 10.1145/3583131.3590416
• 发表时间: 2023
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Mertan, Alican;Cheney, Nick
• 通讯作者: Cheney, Nick

Coping with seasons: evolutionary dynamics of gene networks in a changing environment

应对季节：不断变化的环境中基因网络的进化动态

• DOI: 10.1145/3583133.3590744
• 发表时间: 2023
• 期刊: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
• 作者: Petak, Csenge;Frati, Lapo;Pespeni, Melissa Helen;Cheney, Nick
• 通讯作者: Cheney, Nick