CPS: Medium: Accurate and Efficient Collective Additive Manufacturing by Mobile Robots

CPS：中：移动机器人精确高效的集体增材制造

• 批准号: 1932187
• 负责人: Chen Feng
• 金额: $ 120万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932187&HistoricalAwards=false
• 关键词: CPS; Medium; Accurate; Efficient; Collective

Aging civil infrastructure is a critical worldwide problem that affects daily life, making it important to innovate more efficient and economical repair and construction methods for civil structures. Additive manufacturing, or 3D printing, offers a promising way to fulfill this compelling need. However, almost all current additive manufacturing methods rely on gantry-based systems that can only build structures within rigid frames, thereby restricting printing speed and scale, thus hindering their use in maintenance and construction. This award supports fundamental research to establish collective additive manufacturing, a novel robotics-based approach for large-scale 3D printing. Collective additive manufacturing uses a team of autonomous mobile robots to jointly print large-scale 3D structures. The results of the research will have a potentially wide range of applications in civil infrastructure maintenance and construction, to post-disaster response and extraterrestrial construction. The project is based on a convergent research approach involving robotics, artificial intelligence, control theory, and dynamical systems, which culminates in formal and informal learning activities to broaden participation of underrepresented groups in engineering.Collective additive manufacturing envisions the use of teams of mobile robots to overcome key limitations of existing gantry-based additive manufacturing, including its small scale and slow printing speed. To unleash the full potential of collective additive manufacturing, several scientific boundaries must be pushed, ensuring optimal deployment of multiple mobile robots that print large structures according to an engineered, virtual design. This research will fill critical knowledge gaps in robotic localization, control, and coordination, to realize a robotic team that intentionally and actively modifies its surroundings to successfully complete its printing task. This interdisciplinary research program will unfold along three thrusts: artificial intelligence for planning and localization, model predictive control to adapt to printing disturbances and substrate variations, and distributed control to elicit stable collective dynamics. Theoretical advancements will proceed alongside with experimental research toward demonstrating the potential of collective additive manufacturing to accurately and efficiently print large structures in real-world settings.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.

衰老的民用基础设施是一个关键的全球问题，会影响日常生活，使创新更有效，经济的维修和建造方法至关重要。添加剂制造或3D打印提供了满足这种诱人需求的有希望的方法。但是，几乎所有当前的添加剂制造方法都依赖于基于龙门的系统，这些系统只能在刚性框架内构建结构，从而限制打印速度和规模，从而阻碍其在维护和构造中的使用。该奖项支持基础研究建立集体添加剂制造，这是一种基于机器人的新型3D打印方法。集体添加剂制造使用一组自动移动机器人团队共同打印大型3D结构。这项研究的结果将在民用基础设施维护和构造中具有广泛的应用，用于污水后的响应和外星施工。该项目基于涉及机器人技术，人工智能，控制理论和动态系统的收敛研究方法，该方法最终在正式和非正式的学习活动中，以扩大代表性不足的群体的参与工程。集体增强性制造业设想使用的移动机器人团队的使用来使用基于Gantry Addvility Addvoritival Small Spegs speed Its Speed Speed Ints Speed Ints Speed Ints Syper Speed Inters speed Its speed Its Speed Its speed Its speed Its speed Its speed Its speed Its speed speed speed speeded，并加快了速度。为了释放集体添加剂制造的全部潜力，必须推动几个科学界限，以确保根据工程设计的虚拟设计打印大型结构的多个移动机器人的最佳部署。这项研究将填补机器人本地化，控制和协调方面的关键知识空白，以实现一个有意并积极修改其周围环境以成功完成其印刷任务的机器人团队。该跨学科研究计划将沿三个推力展开：用于计划和本地化的人工智能，模型预测控制以适应印刷干扰和底物变化，并分布式控制以引发稳定的集体动态。理论的进步将与实验研究一起进行，以证明集体增材制造的潜力准确有效地在现实世界中打印大型结构。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Projector-Guided Non-Holonomic Mobile 3D Printing

• DOI: 10.1109/icra48506.2021.9561719
• 发表时间: 2021-05
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Xuchu Xu;Ziteng Wang;Chen Feng

Deep Weakly Supervised Positioning for Indoor Mobile Robots

室内移动机器人的深度弱监督定位

• DOI: 10.1109/lra.2021.3138170
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Wang, Ruoyu;Xu, Xuchu;Ding, Li;Huang, Yang;Feng, Chen
• 通讯作者: Feng, Chen

Towards 3D Perception and Closed-Loop Control for 3D Construction Printing

• DOI: 10.22260/isarc2020/0219
• 发表时间: 2020-10
• 期刊: Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC)
• 作者: Xuchu Xu;Ruoyu Wang;Qiming Cao;Chen Feng

Learning Distilled Collaboration Graph for Multi-Agent Perception

• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Yiming Li;Shunli Ren;Pengxiang Wu;Siheng Chen;Chen Feng;Wenjun Zhang

Learning Simultaneous Navigation and Construction in Grid Worlds

• 发表时间: 2023
• 期刊: ArXiv
• 作者: Wenyu Han;Haoran Wu;Eisuke Hirota;Alexander Gao;Lerrel Pinto;L. Righetti;Chen Feng