EAGER: GOALI: Distributed Embedded Vision System for Multi-Unmanned Ground Vehicle Coordination in Indoor Environments

EAGER：GOALI：用于室内环境中多无人地面车辆协调的分布式嵌入式视觉系统

• 批准号: 1547934
• 负责人: Christophe Bobda
• 金额: $ 27.36万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547934&HistoricalAwards=false
• 关键词: EAGER; GOALI; Distributed; Embedded; Vision

This collaborative project between academics and industry provides a configurable and portable vision-based infrastructure for decentralized coordination of industrial trucks, also known as Automated Guided Vehicles (AGVs) in indoor environments. AGVs have the potential to revolutionize operations in areas such as manufacturing and distribution, health care, and military by efficiently accomplishing the mundane and often repetitive task of transporting materials in distribution and manufacturing. The research addresses limitations of recent successful introduction of commercial robot systems in distribution centers and manufacturing to transport items, but typically only to specific and restrictive environments. The research extends these technologies to environments that are quickly reconfigured by moving stations and objects around. Generic models, tools, and technologies are developed to actively capture the world with semantically labeled objects, actions and events, and to generate goals, priorities, and plans. The solutions proposed in this research address the requirements of decentralized coordination, real-time environmental changes. The approach uses a set of distributed ceiling-mounted smart cameras with overlapping field-of-view for global view and coordination at the facility level, and cameras mounted on AGVs for short-range truck navigation. Multi-truck coordination is then framed as the problem of routing packets in a dynamic and hierarchical network where cameras represent routers and trucks represent packets. To address the complexity of image processing tasks, the hardware/software implementation utilizes an FPGA-based target platform from a previous NSF-Funded project, where low-level inherent parallel image processing tasks are mapped onto hardware while high-level reasoning is kept in software. This project will provide an interface formalism to specify component integration and system composition along with methods to optimize run-time and resource usage. This research is expected to produce an infrastructure and methods for decentralized vision-based coordination of autonomous vehicles activities in a dynamically changing indoor environment. The developed infrastructure will enable manufacturing and distribution companies, including lean and agile entities, to optimize indoor transportation activities in existing arrangement, without modification of available infrastructure, and reduce labor and operating costs by redeploying employees to value-added roles. In addition, AGVs have the potential to enable autonomous mobile robot applications in numerous other unstructured environments, including hospitals, malls, retail stores, critical infrastructure, airports, schools, and sports venues. The project is conducted as a joint effort between the University of Arkansas in Fayetteville and R-Dex in Atlanta and will provide undergraduate and graduate students opportunities to perform their work in academic and industrial environments. The involvement of under-represented groups will be increased with the support of the University of Arkansas Engineering Career Awareness Program, and the University of Arkansas chapters of the National Society of Black Engineer (NSBE) and the Society of Hispanic Professional Engineers (SHPE).

学术界与行业之间的合作项目为工业卡车的分散协调提供了可配置和基于便携式视觉的基础设施，在室内环境中也称为自动化导向车辆（AGV）。 AGV有可能通过有效地完成分销和制造业运输材料的平凡且经常重复的任务来彻底改变制造和分配，医疗保健和军事领域的运营。该研究解决了最近成功地引入分销中心和制造业中商业机器人系统以运输物品的局限性，但通常仅用于特定和限制性的环境。 该研究将这些技术扩展到通过移动站点和周围物体快速重新配置的环境。开发通用模型，工具和技术是为了用语义标记的对象，动作和事件积极捕捉世界，并生成目标，优先级和计划。本研究中提出的解决方案涉及分散协调，实时环境变化的要求。该方法使用一组分布式天花板安装的智能摄像机，并具有重叠的视野，可在设施级别进行全球视图和协调，并安装在AGV上的摄像机进行短距离卡车导航。然后，多卡克协调被构建为在动态和层次网络中路由数据包的问题，​​在该网络中，相机代表路由器和卡车代表数据包。为了解决图像处理任务的复杂性，硬件/软件实施利用了以前的NSF资助项目中的基于FPGA的目标平台，在该项目中，低级固有的并行图像处理任务被映射到硬件上，而高级推理则保留在软件中。该项目将提供一个接口形式主义，以指定组件集成和系统组成以及优化运行时和资源使用情况的方法。预计这项研究将在动态变化的室内环境中生成基础架构和方法，用于分散的自动驾驶汽车活动协调。开发的基础设施将使包括精益和敏捷实体在内的制造和分销公司在现有安排中优化室内运输活动，而无需修改可用的基础设施，并通过将员工重新部署员工重新部署到增值角色来降低劳动力和运营成本。此外，AGV有可能在许多其他非结构化环境中启用自动移动机器人应用程序，包括医院，购物中心，零售店，关键基础设施，机场，学校和体育场所。该项目是作为Fayetteville的阿肯色大学和亚特兰大的R-Dex之间的共同努力进行的，将为本科生和研究生提供在学术和工业环境中进行工作的机会。在阿肯色大学工程职业意识计划的支持下以及阿肯色大学国家黑人工程师学会（NSBE）和西班牙裔专业工程师协会（SHPE）的支持下，将增加代表性不足的团体的参与。