Collaborative Research: CCRI:NEW: Research Infrastructure for Real-Time Computer Vision and Decision Making via Mobile Robots

合作研究：CCRI：新：通过移动机器人进行实时计算机视觉和决策的研究基础设施

• 批准号: 2120333
• 负责人: Yiran Chen
• 金额: $ 22.96万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120333&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; NEW; Infrastructure

This project will create a research infrastructure for computer vision and real-time control of autonomous mobile robots (both aerial and ground). The infrastructure includes four integrated components: (1) A Purdue laboratory decorated as miniature cities. (2) Simulators that reflect the physical laboratory. (3) Programmable aerial robots with the same interface as the simulators. (4) Sample solutions for research on artificial intelligence, computer vision, and robot control for evaluation and comparison. This infrastructure will be available to the research community in multiple ways: (1) Users can evaluate their solutions with the simulators in a safe virtual environment. (2) Users can upload their control programs and this team will launch the robots inside Purdue's laboratory. Users can observe the robots remotely using the high-speed cameras already deployed in the laboratory. (3) Users can bring their own robots to the laboratory and conduct experiments. (4) This project will create competitions for researchers to demonstrate their solutions using autonomous mobile robots in simulated emergency and rescue scenarios. The competitions will use miniature buildings and people for the robots to recognize and count objects (such as number of people, vehicles, and houses), assess situations (such as the number of collapsed bridges), while avoiding obstacles.This infrastructure will be available for investigating a wide range of research topics, including (1) real-time computer vision and control. The decorated laboratory will allow researchers to evaluate their solutions for real-time vision and control methods using active computer vision, navigation, and semantic segmentation in a three-dimensional environment. (2) simulation of robot fleets. Users can evaluate and improve their methods in a safe virtual environment before deployment. (3) This infrastructure will integrate virtual and physical environments so that solutions running in the simulators can be ported directly to the physical robots for experiments. (4) collision avoidance, multi-robot coordination, emergency response, computer security, and efficient machine learning on embedded systems. (5) agriculture, city planning, emergency response, and inspection of civil structures. This project will build STEM talents because autonomous robots and visual data are naturally appealing to the general public. With the simulators, students at all levels can participate without the cost of purchasing physical robots. This research infrastructure will reduce the barriers to innovations. This infrastructure will also encourage innovations in machine learning that are efficient in energy and can be ported to resource constrained embedded systems such as aerial robots. The project will engage a broader audience including K-12 students as well because of the many applications described above.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将为计算机视觉和自主移动机器人（空中和地面）的实时控制创建一个研究基础设施。该基础设施包括四个集成部分：（1）装饰为微型城市的普渡大学实验室。 (2)反映物理实验室的模拟器。 (3) 与模拟器具有相同接口的可编程空中机器人。 (4)用于人工智能、计算机视觉、机器人控制研究的示例方案进行评估和比较。该基础设施将以多种方式提供给研究界：（1）用户可以在安全的虚拟环境中使用模拟器评估他们的解决方案。 （2）用户可以上传他们的控制程序，该团队将在普渡大学的实验室内启动机器人。用户可以使用实验室中已部署的高速摄像机远程观察机器人。 （3）用户可以携带自己的机器人到实验室进行实验。 (4) 该项目将为研究人员举办竞赛，展示他们在模拟紧急情况和救援场景中使用自主移动机器人的解决方案。比赛将使用微型建筑和人，让机器人识别和计算物体（例如人、车辆和房屋的数量），评估情况（例如倒塌桥梁的数量），同时避开障碍物。该基础设施将可用用于调查广泛的研究主题，包括（1）实时计算机视觉和控制。装饰精美的实验室将允许研究人员在三维环境中使用主动计算机视觉、导航和语义分割来评估他们的实时视觉和控制方法的解决方案。 (2)机器人车队的模拟。用户可以在部署之前在安全的虚拟环境中评估和改进他们的方法。 （3）该基础设施将集成虚拟和物理环境，以便模拟器中运行的解决方案可以直接移植到物理机器人上进行实验。 （4）嵌入式系统上的防撞、多机器人协调、应急响应、计算机安全、高效机器学习。 （五）农业、城市规划、应急响应、土木结构检验。该项目将培养 STEM 人才，因为自主机器人和视觉数据自然对公众有吸引力。有了模拟器，各个级别的学生都可以参与，而无需购买实体机器人。该研究基础设施将减少创新的障碍。该基础设施还将鼓励机器学习方面的创新，这些创新不仅节能，而且可以移植到资源有限的嵌入式系统（例如空中机器人）上。由于上述众多应用，该项目将吸引更广泛的受众，包括 K-12 学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

NASRec：推荐系统的权重共享神经架构搜索

• DOI: 10.1145/3543507.3583446
• 发表时间: 2023-04
• 期刊: the ACM Web Conference 2023
• 作者: Zhang, Tunhou;Cheng, Dehua;He, Yuchen;Chen, Zhengxing;Dai, Xiaoliang;Xiong, Liang;Yan, Feng;Li, Hai;Chen, Yiran;Wen, Wei
• 通讯作者: Wen, Wei