NRI: INT: COLLAB: Robust, Scalable, Distributed Semantic Mapping for Search-and-Rescue and Manufacturing Co-Robots

NRI：INT：COLLAB：用于搜索救援和制造协作机器人的稳健、可扩展、分布式语义映射

• 批准号: 1734362
• 负责人: Dario Pompili
• 金额: $ 42.62万
• 依托单位: Rutgers University New Brunswick
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734362&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Robust; Scalable

The goal of this project is to enable multiple co-robots to map and understand the environment they are in to efficiently collaborate among themselves and with human operators in education, medical assistance, agriculture, and manufacturing applications. The first distinctive characteristic of this project is that the environment will be modeled semantically, that is, it will contain human-interpretable labels (e.g., object category names) in addition to geometric data. This will be achieved through a novel, robust integration of methods from both computer vision and robotics, allowing easier communications between robots and humans in the field. The second distinctive characteristic of this project is that the increased computation load due to the addition of human-interpretable information will be handled by judiciously approximating and spreading the computations across the entire network. The novel developed methods will be evaluated by emulating real-world scenarios in manufacturing and for search-and-rescue operations, leading to potential benefits for large segments of the society. The project will include opportunities for training students at the high-school, undergraduate, and graduate levels by promoting the development of marketable skills.The project will advance the state of the art in robust semantic mapping from multiple robots by 1) developing a new optimization framework that can handle large, dynamic, uncertain environments under significant measurement errors, 2) explicitly allowing and studying interactions and information exchanges with humans with an hybrid discrete-continuous extension of the optimization framework, and 3) allowing an intelligent use and sharing of the limited computational resources possessed by the network of co-robots as a whole by enabling approximations and balancing of the computations. These developments will be driven by two particular case studies: a job-shop (small factory) scenario, where robots and fixed cameras are used to track and assist human workers during production and assembly of parts; and a classic search-and-rescue scenario, where operators use an heterogeneous team of robots to quickly assess damages and to discover survivors. These two applications, when considered together, highlight all the limitations of the currently prevalent geometric mapping solutions, and will be used as benchmarks for the project's results.

该项目的目的是使多个共同机器人能够绘制和了解他们在之间有效合作以及与人类经营者在教育，医疗援助，农业和制造应用方面的环境。该项目的第一个独特特征是环境将以语义建模，也就是说，除了几何数据外，它将包含人解剖标签（例如对象类别名称）。这将通过从计算机视觉和机器人技术中的方法进行新颖，强大的方法来实现，从而使机器人与人类之间的通信更加轻松。该项目的第二个独特特征是，由于添加了人类干预信息而增加的计算负载将通过明智地近似和扩展整个网络的计算来处理。新颖开发的方法将通过模拟制造业和搜索操作中的现实世界情景来评估，从而为社会大部分地区带来潜在的好处。该项目将包括在高中，本科生和研究生水平上培训学生通过促进可销售技能的发展的机会。该项目将通过1）通过多个机器人从多个机器人来促进艺术的状态。1）开发一个新的优化框架，该框架可以处理大型，动态，不确定的环境，在重大的测量误差下进行交流和研究，并允许与人类进行交流，2）与人类的互动相互作用，2）与人类的互动相互作用，2）与人类的互动相互作用2）在优化框架中，以及3）允许整个共同机器人网络所拥有的有限计算资源的智能使用和共享，通过启用近似和平衡计算。这些事态发展将由两个特定的案例研究驱动：一个工作店（小工厂）场景，在该场景中，机器人和固定摄像头用于在生产和组装零件期间跟踪和协助人工工人；以及经典的搜索和救援场景，操作员使用异质机器人团队快速评估损失并发现幸存者。同时考虑这两个应用程序，强调了当前普遍存在的几何映射解决方案的所有局限性，并将用作项目结果的基准。

项目成果

Consistent Multi-Robot Object Matching via QuickMatch

通过 QuickMatch 实现一致的多机器人对象匹配

• 发表时间: 2018
• 期刊: International Symposium on Experimental Robotics
• 作者: Serlin, Zackary;Sookraj, Brandon;Belta, Calin;Tron, Roberto
• 通讯作者: Tron, Roberto