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; 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，以使复杂的动态系统完全自动化。该项目调查了在域中的端到端计划构建（例如航天器飞行）的知识表示和推理机制，其中资源消耗均取决于每个车辆及其环境的计算过程和物理特征。这是一个重要且困难的问题，被机器人和AI社区所忽略了。为了应对这一挑战，该项目旨在开发任务和运动计划体系结构（坦帕），并将其应用于两个现实的问题域：地面运输和航天器任务设计。坦帕由任务和路径计划者，资源调度程序和计划执行人组成，弥合了认知（符号）战略级别和基于物理（连续的）战术级别的推理算法之间的长期差距。单一和编队航天器任务将以科学数据质量和数量，观察成本以及算法复杂性和解决方案最优性之间的权衡而设计。该奖项的教育部分在K-12和高等教育中削减。该项目计划追求工程学和K-12课程增强功能，以鼓励各个级别的计算意识学生，努力朝着类似于数学和科学所取得的基本理解方面的基本理解。向K-12和本科研究人员推广将使用动手的机器人实验将所有年龄段的学生吸引到第二世纪的空中和太空行动中，在这种空气和太空行动中，自治将使以前被认为是不可能的任务。