Intelligent, Real-Time Complex Computing Systems

这是该奖项为期三年的第一年。在机器人装配试验台的背景下，努力集中在机器人、视觉、实时人工智能和实时软件系统方面的协调研究。重点是柔性制造自动化，包括主动感知、计划和实时代理之间的合作活动。该项目研究了灵巧操作和视觉的集成，以及静态和移动机器人与人类之间的合作，并解决了机器人系统在处理不确定性和快速变化的环境和任务（例如发生在短期生产中）方面的当前局限性。活动包括开发多种决议表示，允许在局部自反性和全局组合策略之间进行仲裁；研究实时系统中解的质量和计算速度之间的权衡；建模合作与沟通，实现目标导向行为；分布式机械臂控制中相关环境信息提取的集成体系结构和算法实现主动感知，支持基于模型的、面向目标的制造装配操作感知；构建几何推理的高级符号方法并实施学习机制，该机制基于一般任务类的经验对环境进行建模，以指导感知、规划和多智能体合作。

This is the first year of a three year continuing award. The effort focuses on coordinated research in robotics, vision, real- time AI, and real-time software systems, in the context of a robotic assembly testbed. The emphasis is on flexible manufacturing automation involving active perception, planning, and cooperative activities among agents in real time. The project investigates integration of dextrous manipulation and vision, with cooperation among static and mobile robots and humans, and addresses current limitations of robotic systems in dealing with uncertainties and rapidly changing environments and tasks (such as occur in short-run production). Activities include development of multiple resolution representations which permit arbitration between local reflexive and global combinatoric strategies; studying tradeoffs between solution quality and computational speed in real-time systems; modeling cooperation and communication to achieve goal-oriented behavior; integrating architectures and algorithms for extracting relevant environmental information for control of distributed robotic manipulators; implementation of active perception to support model-based, goal oriented sensing for manufacturing assembly operations; constructing high level symbolic approaches to reasoning about geometry; and implementing learning mechanisms which model the environment based on experience over a general class of tasks to guide perception, planning, and multi-agent cooperation.