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.

该项目的总体目的是利用进化原理，以开发一种新型的复杂软机器人（由兼容的材料构建的机器人），这些机器人可以改变其形状和机械性能，以采用不同的形式，以克服环境挑战，例如不稳定的地形。 动机源于动物在数百万年内的进化中通过变化的身体形状和机械性能在其栖息地中实现有效的运动的方式。 基于先前的数值结果，研究人员假设在形状变形和机械性能方面具有足够的复杂性时，可以实现高度适应的运动。 但是，当前的生物启发的软机器人的研究能力有限。该提案中的一个关键目标是开发创新的软机器人平台，以首次研究这些数字发现。计划的研究有可能显着增强高维软机器人的功能，适应性和价值。在软机器人表现出希望的多个领域，尤其是在检查操作和搜索任务中，更广泛的影响将是重大的。预计该项目还会通过使本科生参与计划的研究活动，开发课程内容和针对软机器人的新课程，从而产生重大的教育影响。外展工作的重点是与小学，中学和高中的研讨会，公开演讲以及与当地社区的合作。该计划的研究有望解锁生物系统的进化秘密，并为下一代节能，适应性，多功能和人类友好的机器人打开大门。为了实现这些目标，研究人员建议开发可转换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学和刚度轨迹进行运动。研究人员采用基于进化计算的学习模型来规避与复杂的软机器人系统相关的维数的诅咒，并开发了一个全面的框架，以使用一系列严格的实验评估来开发最佳和鲁棒的控制方案并进行测试。该项目由鲁棒情报（RI）共同资助，既定的竞争研究（EPSCOR）的既定计划。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。

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