RIA: Parallel Artificial Intelligence Techniques Applied toRobot Planning

RIA：并行人工智能技术应用于机器人规划

• 批准号: 9308308
• 负责人: Diane Cook
• 金额: $ 16.3万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-06-01 至 1996-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9308308&HistoricalAwards=false
• 关键词: RIA; Parallel; Artificial; Intelligence; Techniques

The development of intelligent robot planning techniques is an essential element of future underwater and space exploration and industrial automation. These technologies find application in autonomous underwater and planetary rovers, autonomous rendezvous and docking, operation and maintenance of a nuclear power plant, extravehicular activity, autonomous landers, robotic and telerobotic control and automation, and in the general construction and operation of a laboratory, ship or industrial site. The purpose of this project is to increase the efficiency and effectiveness of robot planning by making use of parallel hardware and machine learning techniques. The project is made up to two component parts, each of which contributes independently and collectively to the automation of robotic activity. The first subproject focuses on developing parallel algorithms for heuristic search, which is a fundamental component in robot planning. The second subproject focuses on incremental modification and reuse of plans by searching for similarities between old and new plans. These two projects have been developed and tested on several search and planning problems. We propose to extend the existing research by improving the parallel search algorithms, developing parallel planning algorithms, and applying dynamic graph match to allow reuse of plans in a dynamically changing environment.//

智能机器人规划技术的发展是一个 未来水下和空间探索的基本要素， 工业自动化. 这些技术应用于 自主水下和行星漫游车，自主会合 以及核电站的对接、运行和维护， 舱外活动、自主着陆器、机器人和 远程机器人控制和自动化，并在一般的建设 以及实验室、船舶或工业现场的操作。 的 该项目的目的是提高效率， 利用并行硬件进行机器人规划的有效性 和机器学习技术。 该项目由两个 每个组成部分都独立贡献， 共同实现机器人活动的自动化。 第一 子项目的重点是开发启发式并行算法 搜索，这是机器人规划的基本组成部分。 的 第二个子项目的重点是增量修改和重用 通过寻找新旧计划之间的相似之处来制定计划。 这两个项目已经在几个搜索引擎上进行了开发和测试。 和规划问题。 我们建议扩展现有的研究 通过改进并行搜索算法， 规划算法，并应用动态图匹配， 在动态变化的环境中重用计划。//