Task-Level Planning and Task-Directed Sensing for Robots in Uncertain Environments

不确定环境中机器人的任务级规划和任务导向感知

• 批准号: 9201699
• 负责人: Bruce Donald
• 金额: $ 23.3万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-04-15 至 1997-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9201699&HistoricalAwards=false
• 关键词: Task; Level; Planning; Directed; Sensing

This is the first year of a three-year continuing award. This research investigates task-level planning for autonomous agents, such as mobile robots, that function in an uncertain environment. These robots typically have very approximate, inaccurate, or minimal models ofthe environment. For example, although the geometry of its environment is crucial to determining its performance, a mobile robot might only have a partial, or local "map" of the world. The research will investigate an approach whereby the robot attempts to acquire the necessary information about the world by planning a series of experiments using the robot's sensors and actuators, and building data structures based on the robot's observations of these experiments. A key feature of this approach is that the experiments the robot performs should be driven by the information demands of the task. This research develops (1) a theory of sensor interpretation and task-directed planning using perceptual equivalence classes, intended to be applicable in highly uncertain or unmodelled environments, such as for a mobile robot; (2) algorithmic techniques for modeling geometric constraints on recognizability, and the building of internal representations (such as maps) using these constraints; (3) explicit encoding of the information requirements of a task using a lattice (information hierarchy) of recognizable sets, which allows the robot to perform experiments to recognize a situation or a landmark; and (4) synthesis of robust mobot programs using the geometric constraints, constructive recognizability experiments, and uncertainty models imposed by the task. This research will also implement the theory and test it on mobile robots in the laboratory.

今年是连续三年获奖的第一年。 这 研究调查了自主代理的任务级规划， 例如移动的机器人，在不确定的环境中工作。 这些机器人通常具有非常近似的、不准确的、或 环境的最小模型。 例如，虽然 环境的几何形状对于确定其 性能，一个移动的机器人可能只有部分，或本地 世界的“地图” 这项研究将探讨一种方法， 由此机器人试图获取必要的信息 通过计划一系列的实验， 机器人的传感器和执行器，并建立基于数据结构 机器人对这些实验的观察。 的一个关键特征 这种方法是，机器人进行的实验应该是 由任务的信息需求驱动。 本研究 开发（1）传感器解释和任务导向的理论 使用感知等效类进行规划，旨在 适用于高度不确定或未建模的环境，例如 对于移动的机器人;（2）用于建模的算法技术 几何约束的可识别性，和建设 使用这些约束的内部表示（如地图）; (3)任务信息需求的显式编码 使用可识别集合的网格（信息层次结构）， 允许机器人进行实验来识别情况， 一个地标;和（4）合成鲁棒的移动机器人程序使用的 几何约束，构造性可识别性实验， 以及任务所施加的不确定性模型。 这项研究将 还实现了理论和测试它的移动的机器人在 实验室