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CAREER: Collective behavior in multi-agent systems with active sensing

职业：具有主动感知的多智能体系统中的集体行为

基本信息

批准号：
1751498
负责人：
Nicole Abaid
金额：
$ 50万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751498&HistoricalAwards=false
关键词：
CAREER Collective behavior multi agent

项目摘要

This Faculty Early Career Development Program (CAREER) project will study how large groups of individual agents may collectively use active sensing to improve on the performance of any one individual. In particular, the project seeks to understand and emulate the way in which swarms of bats use echolocation, which is a type of active sensing. In active sensing, for example sonar or lidar, an agent uses the reflection of an emitted signal to learn about its surroundings. Passive sensing, in contrast, relies on the reflection or emission of signals originating elsewhere, such as vision systems that rely on ambient light, or thermal imaging systems that detect heat. In large bat swarms it has been observed that some members seem to navigate by "eavesdropping" on signals emitted by other bats, instead of generating their own. This observation raises many possibilities, including questions of whether eavesdropping changes the behaviors that the swarm can achieve. This project will address these questions through studies on wild bat swarms, then will employ the results in experiments on multiple mobile robots. Of interest will be the ways that simple rules for active sensing by the collective may give rise to complex emergent behavior. The results of this project will benefit national prosperity and welfare by enabling new capabilities in multi-agent networks of mobile robots, for applications such as search and rescue, surveillance and environmental monitoring, package delivery, and construction. This work will also provide insight into the native behavior of bats, many species of which are critically imperiled due to disease and habitat loss. The project includes engagement with K-12 students across Virginia, and supports the development of educational modules that will be disseminated to classrooms throughout the state. The role of sensing in the collective behavior of multi-agent systems -- and particularly the impact that active and passive sensing may have on emergent phenomena -- is currently unexplored. Robotic systems are increasingly turning to distributed approaches, therefore understanding how to leverage their interaction over sensing channels could represent a paradigm shift in the control of such systems. This project seeks to provide a new vision for the dynamics and control of multi-agent systems by coupling sensing and communication. This project is motivated by the intercepted sensing observed in bat swarms. The research team will collect quantitative data from field experiments with wild gray bats, perform model-free analyses to determine how collective sensing and behavior manifests in bats, use this insight to inform and validate a model of group behavior with active sensing, and create numerical and robotic testbeds for hypothesis-based exploration of these novel collective dynamics.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.

这个教师早期职业发展计划（Career）项目将研究大型个体代理群体如何共同使用主动感知来提高任何一个个体的表现。特别是，该项目试图理解和模拟蝙蝠群使用回声定位的方式，这是一种主动感知。在主动传感中，例如声纳或激光雷达，智能体利用发射信号的反射来了解周围环境。相比之下，被动感知依赖于来自其他地方的信号的反射或发射，例如依赖于环境光的视觉系统，或探测热的热成像系统。据观察，在大型蝙蝠群中，一些蝙蝠似乎通过“窃听”其他蝙蝠发出的信号来导航，而不是自己发出信号。这一观察结果提出了许多可能性，包括窃听是否会改变蜂群的行为。该项目将通过对野生蝙蝠群的研究来解决这些问题，然后将结果应用于多个移动机器人的实验中。令人感兴趣的是，集体主动感知的简单规则可能会产生复杂的紧急行为。该项目的成果将使移动机器人的多智能体网络具备新功能，用于搜索和救援、监视和环境监测、包裹递送和建筑等应用，从而有利于国家的繁荣和福利。这项工作还将深入了解蝙蝠的本地行为，其中许多物种由于疾病和栖息地丧失而处于严重危险之中。该项目包括与弗吉尼亚州的K-12学生合作，并支持教育模块的开发，这些模块将传播到整个州的教室。感知在多智能体系统的集体行为中的作用，特别是主动和被动感知可能对紧急现象的影响，目前尚未得到探索。机器人系统越来越多地转向分布式方法，因此了解如何利用它们在传感通道上的相互作用可能代表着这种系统控制的范式转变。该项目旨在通过耦合传感和通信为多智能体系统的动态和控制提供一个新的愿景。这个项目的动机是在蝙蝠群中观察到的截获感测。研究小组将从野生灰蝙蝠的野外实验中收集定量数据，进行无模型分析，以确定蝙蝠的集体感知和行为是如何表现的，利用这一见解来告知和验证具有主动感知的群体行为模型，并为这些新的集体动力学的基于假设的探索创建数值和机器人测试平台。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。