CAREER: Crowdsourcing for Multirobot Coordination

职业：多机器人协调的众包

• 批准号: 2317145
• 负责人: Nora Ayanian
• 金额: $ 52.5万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317145&HistoricalAwards=false
• 关键词: CAREER; Crowdsourcing; Multirobot; Coordination

Teams of humans are exceptionally good at coordination. Teams of robots, however, are extremely clumsy at coordination, requiring extensive communication and computation. Reliance on this infrastructure poses a significant roadblock to bringing robot teams into real-world applications. This project is pursuing an integrated research, education, and outreach approach for developing novel, data-driven algorithms for multi-robot coordination, inspired by human coordination. As cognitive beings that make decisions based on broad context, memory, and sensing, human capabilities are challenging to transfer to robotics. To facilitate this transfer, the project is developing an online crowdsourcing application that tasks participants with creating a global structure, such as a shape. The application constrains participants to robot-like capabilities by limiting available information and actions. The application will provide a faithful representation of the capabilities of distributed teams of robots, and will be used to gain insights into human coordination that can then be transferred to a multi-robot system.The overarching goal of the proposed work is to develop novel methodologies for multi robot coordination firmly grounded in human collaboration, based on models learned from data collected via a crowdsourced online application. To this end, the research objectives are (1) to explicate the relationship between context (communication and sensing) and outcomes in distributed teams of humans working on tightly coupled tasks using data generated from an online multi-person interface; (2) to identify, using statistical methods, parameters for distributed teams of robots solving similar shared objective problems; (3) to infer, using deep learning architectures, diverse ensembles of coordination models for distributed teams of robots solving tightly coupled problems using the data collected from the crowdsourcing application; and (4) to validate these models by evaluating their success in solving tightly coupled problems using a combination of simulation, hardware, and mixed reality experiments.

人类团队非常擅长协调。然而，机器人团队在协调方面极其笨拙，需要广泛的沟通和计算。对这一基础设施的依赖对将机器人团队带入现实世界的应用构成了重大障碍。该项目正在寻求一种综合的研究、教育和推广方法，以开发受人类协调启发的新颖的、数据驱动的多机器人协调算法。作为根据广泛的背景、记忆和感知做出决定的认知存在，人类的能力很难转移到机器人上。为了促进这种转移，该项目正在开发一个在线众包应用程序，任务是让参与者创建一个全球结构，如形状。该应用程序通过限制可用的信息和操作，将参与者限制在类似机器人的能力范围内。该应用程序将提供分布式机器人团队的能力的真实表示，并将用于深入了解人类协调，然后将其转移到多机器人系统。拟议工作的总体目标是开发牢固基于人类协作的新方法，基于从众包在线应用程序收集的数据中学习的模型。为此，研究目标是(1)使用从在线多人界面产生的数据来阐明在处理紧耦合任务的分布式人类团队中情境(交流和感知)与结果之间的关系；(2)使用统计方法来确定用于解决类似共享目标问题的分布式机器人团队的参数；(3)使用深度学习体系结构来推断用于分布式机器人团队解决紧耦合问题的不同的协调模型集合；以及(4)通过结合模拟、硬件和混合现实实验来评估这些模型在解决紧耦合问题方面的成功，从而验证这些模型。