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.

考虑搜索和救援行动或大规模环境监测等场景，无人机必须自主导航、适应动态障碍物并协作优化其行动。在网络物理系统领域，当努力实现去中心化决策时，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式。值得注意的是，自然界中的有机体，如粘菌和真菌，能够发展出分散的、协调的网络，比工程师更好地优化运输、解决迷宫、远距离检测质量，甚至记住周期性事件。这种方法对于解决动态环境条件和确保群体规模扩大时的可扩展性至关重要。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，为无人机群开发新颖的协作组织算法。 我们的策略涉及将来自网络形成有机体的集体行为见解转移到为个体无人机制定规则。该提案解决了群体协作算法中的关键知识差距，重点关注理解指导从微观群体行为向宏观群体行为转变的原则的科学挑战。在工程方面，它的目标是开发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，并增强超级计算能力以提高可扩展性。新颖之处在于采用自下而上的生物学视角，将模拟数据映射到无人机群的计算和通信限制。此外，该项目填补了公众对工程中群体协调和紧急行为理解的关键教育空白。该计划的开源工具旨在加速群体力学的基础研究并加强各个层面的 STEM 教育。该项目专注于将复杂的概念转化为日常语言，通过专业课程、“Swarm Quest”等虚拟体验影响下一代 STEM 工程师，并通过“你比粘菌更聪明吗？”等展览面向公众。 ”，针对最年轻的观众。该计划涉及指导宾夕法尼亚州的一名高中教师、宾夕法尼亚州立大学的研究生和本科生，以及创建有关网络物理系统出现概念的免费教学材料。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。