NCS-FO: Spatial Intelligence for Swarms Based on Hippocampal Dynamics

该项目汇集了大脑功能理论和机器人群体控制原理，以开发更智能的群体，并更好地理解空间表示、导航和规划背后的神经过程。我们的世界在不断变化，哺乳动物已经进化出了根据需要规划新路径或新策略的认知能力。相比之下，自主机器人不太健壮，并且通常难以在复杂多变的环境中操作。这个研究项目的基础是这样一个想法，即一个群体中的单个机器人可以被认为类似于动物大脑中的神经元，它们相互作用形成动态模式，这些模式共同发出相对于大脑节律的空间和时间位置的信号。这种信息在空间和时间上的分布将使一种新的群体控制模式成为可能，在这种模式下，群体会自动适应世界的变化，就像老鼠知道如何绕过意想不到的障碍一样。无人驾驶机器人正迅速成为全国乃至地球仪商业、军事和科学研究的关键技术。未来的关键应用，如救灾和搜索救援，将需要分布在广阔地理区域的许多机器人之间的智能空间协调。该项目将推进神经群集作为下一代技术发展的控制范例。此外，该项目将推动一个广泛的科学，技术，工程和数学教育计划，将空间智能，海马信息处理和群体控制的概念带给高中学生，以提高神经科学和机器人技术的素养。该项目的目标是建立一个统一的框架，自下而上控制空间任务规划，协同推进理论神经科学和群体控制范式。在该项目的大脑到群体的比喻中，神经元是自主代理，尖峰是基于代理的相位信号，紧急电路活动是紧急群体行为。该方法的目标是海马电路和相关系统中的神经计算，这可能有助于在线动态重新规划。研究重点包括数据驱动的动态网络和神经活动序列的点过程模型，使用矩阵流形对群集动力学进行数学分析，以及在现实虚拟环境中进行自治系统仿真。该项目将促进对紧急海马动力学和动态重新规划的自主方法的理解，激发分布式控制的新研究。该项目的框架可以使大规模的可扩展性，敏捷的简单机器人代理群。这个奖项反映了NSF的法定使命，并已被认为是值得通过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的支持。

This project brings together theories of brain functions and principles of robotic swarm control to develop smarter swarms and to better understand the neural processes underlying spatial representations, navigation, and planning. Our world is constantly changing, and mammals have evolved the cognitive ability to plan new paths or new strategies as needed. By contrast, autonomous robots are less robust, and often have difficulty operating in complex, changing environments. This research project is grounded in the idea that individual robots in a group can be thought of analogously to neurons in an animal's brain, which interact with one another to form dynamic patterns that collectively signal locations in space and time relative to brain rhythms. This distribution of information across space and time will enable a new paradigm of swarm control, in which swarms automatically adapt to changes in the world in the same way that a rat knows which detour to take around an unexpected obstacle. Unmanned robots are rapidly becoming a crucial technology for commercial, military, and scientific endeavors throughout the nation and across the globe. Critical future applications such as disaster relief and search & rescue will require intelligent spatial coordination among many robots spread over large geographical areas. This project will advance neural swarming as a control paradigm for this next generation of technological development. Additionally, this project will drive an extensive science, technology, engineering, and mathematics education program to bring the concepts of spatial intelligence, hippocampal information processing, and swarm control to high school students to improve literacy in neuroscience and robotics.The project's goal is to build a unified framework for self-organized, bottom-up control of spatial task planning that synergistically advances theoretical neuroscience and swarm control paradigms. In the project's brain-to-swarm metaphor, neurons are autonomous agents, spikes are agent-based phase signals, and emergent circuit activity is emergent swarm behavior. The approach targets neural computations in hippocampal circuits and related systems that may contribute to online dynamic replanning. The research thrusts comprise data-driven dynamical network and point-process models of neural activity sequences, mathematical analysis of swarming dynamics using matrix manifolds, and autonomous systems simulations in realistic virtual environments. The project will advance understanding of emergent hippocampal dynamics and autonomous methods for dynamic replanning, motivating new research in distributed control. The project's framework may enable mass-scalability for large, agile swarms of simple robotic agents.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.