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

这个项目研究了一组机器人的自主控制和协调，这些机器人的任务是探索、绘制地图或监控它们所处的环境。该项目旨在通过集成数据压缩感知来增强这样一组机器人的能力。压缩感知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相互交流信息，并智能地指导其运动以获取未来的相关感知数据。为了实现压缩机器人传感系统的潜力，该项目必须解决重大的理论和技术挑战。该项目将在两个具体应用中展示结果，(i)驱动一组空中机器人来监测它们的环境，（ii）驱动机器人微型探针来测量活细胞内部的过程。该项目还设法通过教育和外联活动传播其调查结果。研究结果将被纳入波士顿大学和斯坦福大学控制理论的本科和研究生课程。研究人员还将通过研究指导项目和为参观者提供实验室示范，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，利用稀疏信号描述为监测环境的传感机器人团队创建有效的运动计划。驱动假设是，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——这些都是机器人平台的有限资源。研究小组将采用贝叶斯方法进行压缩感知，这使得感知质量可以用信息理论度量（如熵）来量化。将开发一种后退地平线控制方法，用于驱动机器人传感器收集最有价值的传感器数据，以便使用压缩感知重建其环境的稀疏表示。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的解决方案。本项目开发的概念将应用于两个特定的传感领域：(i)传感环境数据的四旋翼传感机器人网络和（ii）用于生物分子系统动力学三维成像的共聚焦荧光显微镜。这两个应用程序域具有完全不同的长度和时间尺度、动态属性和信息内容。在这个项目中开发的想法在这两个领域的成功应用将证明压缩机器人传感系统概念的普遍性。

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.