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

这是该奖项为期三年的第一年。本研究探讨了在不确定环境中工作的自主代理（如移动机器人）的任务级规划。这些机器人通常具有非常近似的、不准确的或最小的环境模型。例如，尽管环境的几何形状对决定其性能至关重要，但移动机器人可能只有部分或局部的世界“地图”。该研究将研究一种方法，通过使用机器人的传感器和执行器计划一系列实验，并根据机器人对这些实验的观察建立数据结构，机器人试图获取有关世界的必要信息。这种方法的一个关键特征是机器人执行的实验应该由任务的信息需求驱动。本研究发展了(1)使用感知等效类的传感器解释和任务导向规划理论，旨在适用于高度不确定或未建模的环境，例如移动机器人；(2)对可识别性的几何约束进行建模的算法技术，以及利用这些约束构建内部表示（如地图）的算法技术；(3)使用可识别集的格（信息层次）对任务的信息需求进行显式编码，使机器人能够进行识别情景或地标的实验；(4)利用几何约束、建设性可识别性实验和任务施加的不确定性模型综合鲁棒移动机器人程序。本研究还将在实验室中对移动机器人进行理论实施和测试。

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.