EAGER: Toward Supervised Autonomy for Robotic Systems

EAGER：迈向机器人系统的监督自治

• 批准号: 1449505
• 负责人: Erion Plaku
• 金额: $ 15万
• 依托单位: Catholic University of America
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1449505&HistoricalAwards=false
• 关键词: EAGER; Toward; Supervised; Autonomy; Robotic

This project seeks to make the supervision of robotic systems operating in complex domains similar to that of humans so as to increase productivity and capabilities. The software framework resulting from this project frees supervisors from the burden of unnatural low-level commands and instead allows them to describe tasks in a structured language that has the ability to express global and local objectives across time spans. The framework then automatically computes the necessary motions to enable the robotic system accomplish the assigned tasks. This necessitates a comprehensive treatment of planning to account for sophisticated tasks, robot dynamics, collision avoidance, robust replanning, and interactions with human supervisors. To addresses such complexity, the framework employs a novel probabilistic search with discrete abstractions and enhanced sampling capability to focus the search on the space of feasible motions that enable the robotic system to make progress toward accomplishing the assigned task. The framework also provides critical feedback information about the progress made to help supervisors adapt the specifications in response to challenges encountered during planning and execution. This project is expected to establish a new paradigm for supervised autonomy and impact the development of research and commercial software for robotic systems. Doing so has the potential to enhance applications of robotic systems such as underwater vehicles in surveying marine wildlife, inspecting harbors and offshore platforms.

该项目旨在使机器人系统在复杂领域的操作类似于人类的监督，以提高生产力和能力。该项目产生的软件框架将管理人员从不自然的低级命令的负担中解放出来，而是允许他们用结构化语言描述任务，这种语言能够跨时间跨度表达全局和局部目标。然后，框架自动计算必要的运动，使机器人系统完成指定的任务。 这就需要对规划进行全面的处理，以考虑复杂的任务、机器人动力学、碰撞避免、鲁棒的重新规划以及与人类主管的交互。为了解决这样的复杂性，该框架采用了一种新的概率搜索与离散的抽象和增强的采样能力，使机器人系统朝着完成指定的任务取得进展的可行运动的空间上的搜索。该框架还提供了有关进展情况的重要反馈信息，以帮助主管根据规划和执行过程中遇到的挑战调整规格。 预计该项目将为监督自治建立一个新的范式，并影响机器人系统研究和商业软件的开发。这样做有可能加强机器人系统的应用，如水下航行器在调查海洋野生动物，检查港口和海上平台。