RI-Small: Reconfigurable and Adaptable Multi-Robot Coalitions

RI-Small：可重构和适应性强的多机器人联盟

• 批准号: 0812117
• 负责人: Lynne Parker
• 金额: $ 35.41万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812117&HistoricalAwards=false
• 关键词: RI; Small; Reconfigurable; Adaptable; Multi

This project is aimed at substantially increasing the flexibility, autonomy, and system robustness of heterogeneous multi-robot teams by changing the fundamental abstraction that is used to represent robot competences from the typical "task" abstraction to our proposed ?schema? abstraction. The inputs and outputs of these schemas are characterized in terms of their semantic information content, which our work exploits to automatically reconfigure the schemas to address the teaming task at hand. This new abstraction allows humans to design task-independent software that can be autonomously reconfigured by the robot team in response to dynamic changes in the environment, and the current task needs of the team. In doing this, we are able to simultaneously obtain a number of significant, wide-ranging new benefits in coalescent multi-robot teaming, including: (1) enabling robots to generate solutions to new tasks that were not explicitly programmed by the human designer, but instead consist of new, automated combinations of low-level building blocks, or schemas; (2) enabling robot team members to automatically generate coalescent task solutions based on sensor-sharing across team members, in configurations not previously explicitly defined by the human designer; and (3) enabling flexible software code reuse from one robot teaming application to another.

该项目的目的是通过改变用于代表机器人能力的基本抽象，从典型的“任务”抽象到我们提出的大幅度增加的灵活性，自主性和系统的鲁棒性的异构多机器人团队？图式抽象这些模式的输入和输出的特点是在他们的语义信息内容，我们的工作利用自动重新配置的模式，以解决手头的组队任务。 这种新的抽象允许人类设计与任务无关的软件，这些软件可以由机器人团队自主重新配置，以响应环境的动态变化和团队当前的任务需求。通过这样做，我们能够同时获得联合多机器人团队的许多重要的，广泛的新好处，包括：（1）使机器人能够生成新任务的解决方案，这些任务不是由人类设计师显式编程的，而是由低级构建块或模式的新的自动组合组成;（2）使机器人团队成员能够在人类设计者先前未明确定义的配置中基于跨团队成员的传感器共享来自动生成联合任务解决方案;以及（3）使灵活的软件代码能够从一个机器人团队应用重用到另一个机器人团队应用。