权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

NRI: INT: COLLAB: Co-Robotic Systems for GeoSciences Field Research

NRI：INT：COLLAB：用于地球科学领域研究的协作机器人系统

基本信息

批准号：
1734355
负责人：
Daniel Koditschek
金额：
$ 107.61万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734355&HistoricalAwards=false
关键词：
NRI INT COLLAB Co Robotic

项目摘要

Sand and dust storms are a growing worldwide menace, soil instability and erosion threatens agriculture, and fine sediment compromises ecosystem health of rivers and oceans, all impacting large human populations on nearly every continent. The cumulative effects of these disturbed environments also threaten human well-being through damage of habitation and disruption of transportation. An interdisciplinary collaboration of geoscience, cognitive science, and robotics researchers aims to accelerate and deepen the collection of data about the fluid and materials properties associated with such unstable soils by endowing legged robots with the instrumentation and scientific agenda of agile, novice field assistants. These new robots are capable of general field mobility and are being programmed to think like assistant field geologists in order to develop research strategies in rugged natural environments where measurements are lacking. Overcoming the specific locomotion challenges presented by these environments and developing algorithms and software sufficient to interpret and act on human research needs will greatly advance the field of robotics. The resulting new information about wind, water and materials processes will have bearing on the management of infrastructure and agriculture, and also the response of landscapes to environmental changes.The project focus is to use the geosciences field research setting to test a chain of hypotheses reaching from the formal representation of scientific knowledge to the properties of provably correct algorithms for human-machine pursuit of scientific data. The geoscientific goal is to produce the first comprehensive, time varying maps of soil strength with co-located soil moisture composition and size over the course of rainfall events in a natural landscape. The cognitive science goal is to develop a formalized representation of the cognitive processes underlying field data collection and interpretation that is simultaneously suitable to underlie robotic field assistance algorithms while at the same time advancing the study of human perceptual interpolation and reasoning. The robotics goal is to achieve a provably correct architecture for generating from formalized human task specification a chain of safe, stable online automated legged gait transitions on complex broken terrain that subserve the geoscientist data collection objectives. Two different families of legged robots with a variety of perceptual and geoscientific instrumentation suites will be deployed over natural hillsides under investigation by human geoscientists. Field performance of the resulting human-robot teams will be evaluated according to criteria assessing the degree of robotic mobility and autonomy, the quality and reliability of the resulting geoscientific measurements, and the impact of the collection process on the sampled environment. Advances in legged robot mechanics and intelligent control have brought the field to a threshold where the next major challenges for autonomous mobility can only be formulated and engaged with respect to suites of abstract but formal tasks relative to unstructured environments against which the appropriateness and success of autonomously generated, goal-directed motor behaviors can be precisely measured.Robots endowed with even the rudiments of understanding what measurements are needed where and when by scientists, in order to test their hypotheses, would deepen our insight into the structure of human cognition. They would also open the way toward collecting massive amounts of data at presently unachievably fine spatiotemporal scales, potentially transforming the theoretical and empirical foundations of geoscience.

沙尘暴是一个日益严重的世界性威胁，土壤不稳定和侵蚀威胁着农业，细小的沉积物危及河流和海洋的生态系统健康，所有这些都影响着几乎每个大陆的大量人口。这些受干扰的环境的累积影响还通过破坏居住和扰乱交通来威胁人类福祉。地球科学、认知科学和机器人研究人员的跨学科合作旨在加快和深化与这种不稳定土壤相关的流体和材料属性数据的收集，方法是为腿部机器人提供灵活、新手的现场助理的仪器和科学议程。这些新的机器人能够进行一般的野外移动，并被编程为像助理野外地质学家一样思考，以便在缺乏测量的崎岖自然环境中制定研究策略。克服这些环境带来的特定运动挑战，并开发出足以解释和满足人类研究需求的算法和软件，将极大地推动机器人领域的发展。由此产生的关于风、水和物质过程的新信息将对基础设施和农业的管理以及景观对环境变化的响应产生影响。项目重点是利用地球科学的实地研究背景来测试一系列假设，从科学知识的形式表示到用于人机追求科学数据的可证明正确的算法的性质。地球科学的目标是制作第一张全面的、随时间变化的土壤强度地图，在自然景观中的降雨过程中，土壤水分组成和大小是同一地点的。认知科学的目标是开发一种支持现场数据收集和解释的认知过程的形式化表示，同时适用于机器人现场辅助算法，同时促进人类知觉内插和推理的研究。机器人的目标是实现一个可证明是正确的体系结构，以便从形式化的人类任务规范中生成一系列安全、稳定的在线自动步态过渡，在复杂的破碎地形上，为地球科学家的数据收集目标服务。在人类地球科学家的调查下，两个不同系列的腿机器人将被部署在自然山坡上，这些机器人带有各种感知和地球科学仪器套件。将根据评估机器人机动性和自主性程度、由此产生的地球科学测量的质量和可靠性以及收集过程对采样环境的影响的标准来评估所产生的人类-机器人小组的实地表现。腿部机器人力学和智能控制的进步将该领域带入了一个门槛，下一个主要的自主移动性挑战只能针对与非结构化环境相关的一套抽象但正式的任务来制定和实施，在这些任务中，可以精确地衡量自主生成的、以目标为导向的运动行为的适当性和成功。机器人甚至被赋予了理解何时何地需要什么测量的基本知识，以检验科学家的假设，这将加深我们对人类认知结构的洞察。它们还将为在目前无法实现的精细时空尺度上收集大量数据开辟道路，潜在地改变地球科学的理论和经验基础。