ITR - (NHS+ASE+ECS) - (dmc+sim+int): Loosely Cooperating Micro Air Vehicle Networks for Toxic Plume Characterization

提案编号：0427947 PI： Kamran Mohseni机构： 科罗拉多大学，博尔德标题：ITR -松散合作的微型飞行器网络的有毒羽流表征摘要：在城市地区释放污染物可能导致严重的公共卫生后果。该项目开发了一种集成的传感器群，能够快速表征有毒羽流，用于污染预测和源定位。传感器群由半自主微型飞行器（MAV）组成，在人口稠密地区的大气中运输微型毒素传感器，与基站联网，提供毒素扩散建模和群监督，使用新型轻量级，实时，数据反应式无线信息路由。MAV平台降低了制造成本，减少了碰撞损坏的风险，并降低了可能引起公众恐慌的能见度和噪音。信息能源的概念被引入到三个跨学科领域的紧密结合的进步：空气动力学，网络和控制。 这使得高质量的信息产品从传感器羊群，使用相对简单的个人车辆。 MAV是根据蝙蝠飞行的仿生原理设计的，以实现卓越的飞行性能。 车辆控制是基于一个分层的信息能量公式，产生简单的梯度下降制导律的每辆车，但与良好理解的群集行为向区域的高质量数据。数据驱动路由的行为在以下三个通信场景中的每一个进行了研究：MAV之间的交换高质量的数据群收敛; MAV到地面的传感器数据收集;和地面到MAV的命令和控制。在CU博尔德现有的学科课程丰富了这项工作的结果，扩大学生的多学科接触。 一个夏季航空机器人计划使当地高中学生能够参加一个融合计算机科学和航空航天工程的设计/建造/飞行竞赛。

Proposal Number: 0427947PI: Kamran Mohseni Institution: University of Colorado, Boulder Title: ITR - Loosely Cooperating Micro Air Vehicle Networks for Toxic Plume CharacterizationAbstract: Release of contaminants in urban areas can lead to severe public health consequences. This project develops an integrated Sensor Flock to enable rapid characterization of toxic plumes for contamination prediction and source location. A Sensor Flock consists of semi-autonomous micro air vehicles (MAVs) transporting miniature toxin sensors throughout the atmosphere above populated areas, networked to a base station providing toxin dispersion modeling and flock supervision, using novel lightweight, real-time, data-reactive wireless information routing. MAV platforms lower manufacturing costs, reduce risks of collision damage, and reduce visibility and noise that might create public alarm. The concept of Information Energy is introduced to tightly integrate advances in three interdisciplinary areas: aerodynamics, networking, and control. This enables high-quality information products from the Sensor Flock, using relatively simple individual vehicles. The MAVs are designed based on biomimetic principles of bat flight to enable exceptional flight performance. Vehicle control is based on a hierarchical information energy formulation, producing simple gradient-descent guidance laws on each vehicle, but with well-understood flock clustering behavior toward regions of high-quality data. The behavior of data-driven routing in each of the following three communication scenarios is investigated: inter-MAV exchange of high-quality data for flock convergence; MAV-to-ground collection of sensor data; and ground-to-MAV command and control. Existing disciplinary courses at CU Boulder are enriched with results from this work, expanding student multidisciplinary exposure. A summer Aerobotics program enables local high school students to participate in a design/build/fly competition integrating computer science and aerospace engineering.