CAREER: Transferring biological networks emergent principles to drone swarm collaborative algorithms

职业：将生物网络新兴原理转移到无人机群协作算法

• 批准号: 2339373
• 负责人: Christian Peco Regales
• 金额: $ 54.94万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339373&HistoricalAwards=false
• 关键词: CAREER; Transferring; biological; networks; emergent

Consider scenarios like search and rescue operations or large-scale environmental monitoring where drones must autonomously navigate, adapt to dynamic obstacles, and collaboratively optimize their actions. In the field of cyberphysical systems, the challenge intensifies when striving for decentralized decision-making. The successful development of these algorithms can produce potentially transformative paradigms for applications critical to societal welfare. Remarkably, organisms in nature, such as slime molds and fungi, are able to develop decentralized, coordinated networks that optimize transport better than engineers, solve mazes, detect masses at a distance, or even memorize periodic events. This approach is pivotal in addressing dynamic environmental conditions and ensuring scalability as the swarm size expands. This research aims to develop novel collaborative organization algorithms for drone swarms by leveraging advanced computational frameworks that mimic the functionalities of biological networks. Our strategy involves transferring collective behavior insights from network-forming organisms to formulate rules for individual drones. The proposal addresses critical knowledge gaps in swarm collaborative algorithms, focusing on the scientific challenge of understanding the principles guiding the transition from microscale to macroscale swarm behavior. On the engineering front, it aims to develop robust machine learning procedures for accurately transferring observed behaviors to synthetic systems and enhance supercomputing capabilities for improved scalability. The novelty lies in adopting a bottom-up biological perspective, mapping simulation data showcasing emergence to the computational and communication constraints of a drone swarm. Additionally, this project fills critical educational gaps in the public understanding of swarm coordination and emergent behavior in engineering. The initiative's open-source tools aim to accelerate basic research in swarm mechanics and enhance STEM education at various levels. With a focus on translating complex concepts into everyday language, the project impacts the next generation of STEM engineers through specialized courses, virtual experiences like "Swarm Quest", and also targets the general public with exhibitions like "Are you smarter than a slime mold?", aimed at the youngest audience. The initiative involves mentoring a high-school teacher in Pennsylvania, graduate and undergraduate students at Penn State, and the creation of free instructional material on the concept of emergence in cyberphysical systems.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.

考虑诸如搜索和救援操作或大规模环境监视之类的场景，无人机必须自动导航，适应动态障碍并协作优化其行动。在网络物理系统领域，在争取分散决策时，挑战会加剧。这些算法的成功开发可以为对社会福利至关重要的应用产生潜在的变革范例。值得注意的是，自然界中的生物，例如粘液模具和真菌，能够开发出分散的，协调的网络，这些网络比工程师更好地优化运输，解决迷宫，在距离上检测质量，甚至记住定期事件。这种方法在解决动态环境条件方面至关重要，并确保随着群尺寸的扩展而确保可伸缩性。这项研究旨在通过利用模仿生物网络功能的先进计算框架来开发针对无人机群的新型合作组织算法。 我们的策略涉及将集体行为洞察力从建立网络的生物中转移，以制定单个无人机的规则。该提案解决了群体协作算法中的关键知识差距，重点是理解指导从微观科学到宏观群体行为的过渡的原理的科学挑战。在工程方面，它旨在制定强大的机器学习程序，以准确地将观察到的行为转移到合成系统并增强超级计算功能以提高可伸缩性。新颖性在于采用自下而上的生物学观点，将模拟数据映射到无人机群的计算和通信约束。此外，该项目填补了对公众对工程群体协调和紧急行为的公众理解的关键教育空白。该计划的开源工具旨在加快群体力学的基础研究，并在各个层面增强STEM教育。该项目着重于将复杂概念转化为日常语言，通过专门的课程，“ Swarm Quest”等虚拟经验影响下一代STEM工程师，并以“您比史莱姆模具更聪明？该倡议涉及指导宾夕法尼亚州的一名高中老师，宾夕法尼亚州立大学的毕业生和本科生，以及创建有关网络物理体系中出现概念的免费教学材料。这项奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛的crietia cribity criteria criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia recectia recteria均值得一提。