CONE: Collaborative Observatory for Natural Environments

这个项目是由来自德克萨斯州A M和加州大学伯克利分校的计算机科学家和工程师与自然科学家和纪录片制片人共同努力的结果。 目标是推进对自动化和协作系统的基本理解，这些系统结合了联合收割机传感器、执行器和人类输入，以观察和记录远程环境中的详细自然行为。 目前，对动物的原位科学研究需要在数周或数月内对动物的详细行为进行警惕的观察。 当动物生活在偏远和/或不适宜居住的地方时，观察对科学家来说可能是一种艰苦、昂贵、危险和孤独的经历。 该项目提出了一种新型的混合遥控/自主机器人“观测站”，允许科学家通过互联网远程观察，记录和索引详细的动物活动。 这些观测站是由于机器人相机、远程无线网络和分布式传感器的新兴进步而成为可能的。 该项目将研究这些观测站的算法基础：新的指标，模型，数据结构和算法，这将构成一个强大的数学框架，用于协作观测。 该项目将在过去工作的基础上，扩展和正式表征利用计算几何、随机建模和优化的协作和自动观察的混合模型。 该项目将推进对网络机器人的基本理解，并开发结合人类和传感器输入的协作观察的有效算法。 这项工作旨在使生物科学家受益，并促进研究人员之间的合作。 它将产生可通过互联网访问的工作原型，供世界各地的科学家，学生和公众使用。

This project is a collaborative effort by computer scientists and engineers from Texas A&M and UC Berkeley consulting with natural scientists and documentary filmmakers. The goal is to advance the fundamental understanding of automated and collaborative systems that combine sensors, actuators, and human input to observe and record detailed natural behavior in remote settings. Currently, scientific study of animals in situ requires vigilant observation of detailed animal behavior over weeks or months. When animals live in remote and/or inhospitable locations, observation can be an arduous, expensive, dangerous, and lonely experience for scientists. The project proposes a new class of hybrid teleoperated/autonomous robotic "observatories" that allow groups of scientists, via the internet, to remotely observe, record, and index detailed animal activity. Such observatories are made possible by emerging advances in robotic cameras, long-range wireless networking, and distributed sensors. The project will investigate the algorithmic foundations for such observatories: new metrics, models, data structures, and algorithms, that will comprise a robust, mathematical framework for collaborative observation. The project will build on past work to extend and formally characterize hybrid models of collaborative and automated observation that draw on computational geometry, stochastic modeling and optimization. The project will advance fundamental understanding of networked robotics and develop efficient algorithms for collaborative observation that combines human and sensor input. This effort is intended to benefit biological scientists and facilitate collaboration among researchers. It will produce working prototypes that will be accessible via the internet to scientists, students, and the public worldwide.