Collaborative Research: Compressive Robotic Systems: Gaining Efficiency Through Sparsity in Dynamic Environments

协作研究：压缩机器人系统：通过动态环境中的稀疏性提高效率

基本信息

批准号：
1562031
负责人：
Sean Andersson
金额：
$ 30万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562031&HistoricalAwards=false
关键词：
Collaborative Research Compressive Robotic Systems

项目摘要

This project investigates autonomous control and coordination of a group of robots that are tasked to explore, map, or monitor the environment they are in. The project aims to enhance the capabilities of such a group of robots by integrating Compressive Sensing for data compression. Compressive sensing enables robots to quickly extract information from their environment, efficiently communicate that information to each other over a wireless network, and intelligently direct their motion to obtain relevant sensing data in the future. Significant theoretical and technical challenges must be addressed in this project to realize the potential of a compressive robotic sensing system. The project will demonstrate results in two specific applications, (i) driving a group of aerial robots to monitor their environment, (ii) driving robotic micro-probes to measure processes inside a living cell. The project also seeks to disseminate its findings through educational and outreach activities. Results will be incorporated into undergraduate and graduate level courses in control theory at both Boston University and Stanford University. The researchers will also work with high school students and undergraduates through research mentorship programs and through lab demonstrations for visitors.The fundamental goal of the project is to create rigorously analyzed algorithms that take advantage of sparse signal descriptions to create efficient motion plans for a team of sensing robots that monitor the environment. The driving hypothesis is that sparsity can greatly extend the performance of robotic sensing systems by saving battery power, computation, storage, and communication bandwidth---all critically limited resources for robotic platforms. The research team will take a Bayesian approach to Compressive Sensing, which allows for sensing quality to be quantified with information theoretic metrics such as entropy. A receding horizon control approach will be developed for driving robotic sensors to collect the most valuable sensor data, in order to reconstruct a sparse representation of their environment using Compressive Sensing. Such control strategies will be adapted to both static and dynamic environments, and both centralized and distributed solutions will be sought. The concepts developed in this project will be applied to two specific sensing domains: (i) networks of quadrotor sensing robots sensing environmental data and (ii) confocal fluorescence microscopy for three-dimensional imaging of dynamics in bio-molecular systems. These two application domains have radically different length and time scales, dynamical properties, and information content. A successful application of the ideas developed in this project to both these domains will prove the generality of the Compressive Robotic Sensing System concept.

该项目调查了一组机器人的自主控制和协调，这些机器人要探索，映射或监视其所处环境。该项目旨在通过集成数据压缩的压缩感应来增强此类机器人的能力。压缩传感使机器人能够从其环境中快速提取信息，通过无线网络有效地互相传达信息，并智能地指导其运动以在将来获得相关的感应数据。在该项目中必须解决重大的理论和技术挑战，以实现压缩机器人传感系统的潜力。该项目将在两个特定的应用中展示结果：（i）驱动一组空中机器人监视其环境，（ii）驱动机器人微型探测器以测量活细胞内的过程。该项目还旨在通过教育和外展活动传播其发现。结果将纳入波士顿大学和斯坦福大学控制理论的本科和研究生级课程。研究人员还将通过研究指导计划和访问者的实验室演示与高中生和本科生合作。该项目的基本目标是创建严格分析的算法，以利用稀疏信号描述，以创建有效的运动计划，以为监控环境的一组感应机器人创建有效的运动计划。驾驶假设是，稀疏性可以通过节省电池电量，计算，存储和通信带宽来极大地扩展机器人传感系统的性能 - 所有机器人平台的资源都非常有限。研究团队将采用贝叶斯方法来进行压缩感测，从而可以通过信息理论指标（例如熵）来量化传感质量。将开发一种恢复的地平线控制方法，用于驱动机器人传感器收集最有价值的传感器数据，以便使用压缩传感重建对环境的稀疏表示。这种控制策略将适应静态和动态环境，并将寻求集中和分布式解决方案。该项目中开发的概念将应用于两个特定的传感域：（i）四个感应机器人的网络感应环境数据和（ii）共聚焦荧光显微镜，用于生物分子系统中动力学的三维成像。这两个应用程序域具有根本不同的长度和时间尺度，动力学属性和信息内容。该项目在这两个领域中开发的思想的成功应用将证明压缩机器人传感系统概念的一般性。