CAREER: State-dependent Resource Management for Integrated Task and Motion Plans

职业：集成任务和运动计划的状态相关资源管理

• 批准号: 0732592
• 负责人: Ella Atkins
• 金额: --
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-15 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0732592&HistoricalAwards=false
• 关键词: CAREER; State; dependent; Resource; Management

Low-level motion control and high-level decision-making algorithms must be integrated to enable full automation of complex dynamic systems. This project investigates knowledge representation and inference mechanisms for end-to-end plan construction in domains, such as spacecraft flight, where resource consumption is dependent both on computational processes and physical characteristics of each vehicle and its environment. This is an important and difficult problem that has been somewhat neglected by the robotics and AI communities. To address this challenge, the project aims to develop a Task and Motion Planning Architecture (TAMPA) and apply it to two realistic problem domains: ground transport and spacecraft mission design. TAMPA is composed of task and path planners, resource scheduler, and plan executor, bridging the long-standing gap between cognitive (symbolic) strategic-level and physics-based (continuous) tactical-level reasoning algorithms. Single and formation spacecraft missions will be designed in terms of science data quality and quantity, observation costs, and tradeoffs between algorithm complexity and solution optimality.The education component of this award cuts across K-12 and higher education. This project plans to pursue engineering and K-12 curriculum enhancements to encourage computationally-aware students at all levels, working toward a basic understanding analogous to that achieved for math and science. Outreach to K-12 and undergraduate researchers will use hands-on robotic experiments to draw students of all ages into a second century of air and space operations in which autonomy will enable missions previously considered impossible.

必须集成低级运动控制和高级决策算法，以实现复杂动态系统的完全自动化。该项目研究了在航天器飞行等领域中端到端计划构建的知识表示和推理机制，其中资源消耗取决于每个飞行器及其环境的计算过程和物理特性。这是一个重要而困难的问题，但在某种程度上被机器人和人工智能社区所忽视。为了应对这一挑战，该项目旨在开发一个任务和运动规划架构（坦帕），并将其应用到两个现实的问题域：地面运输和航天器使命设计。坦帕由任务和路径规划器、资源调度器和计划执行器组成，弥合了认知（符号）战略级和基于物理（连续）战术级推理算法之间的长期差距。单个和编队航天器任务将根据科学数据的质量和数量、观测成本以及算法复杂性和解决方案最优性之间的权衡来设计。该奖项的教育部分横跨K-12和高等教育。该项目计划追求工程和K-12课程的增强，以鼓励各级计算意识的学生，努力实现类似于数学和科学的基本理解。对K-12和本科研究人员的宣传将使用动手机器人实验，吸引所有年龄段的学生进入第二个世纪的空中和太空操作，在这个世纪中，自主权将使以前被认为是不可能的任务成为可能。