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

这是为期三年的连续奖项的第一年。这项研究研究了自主代理的任务级规划，如移动机器人，这些代理在不确定的环境中工作。这些机器人通常对环境有非常近似、不准确或最小的模型。例如，尽管环境的几何形状是决定其性能的关键，但移动机器人可能只有世界的部分或局部“地图”。这项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，机器人试图获取关于世界的必要信息。这种方法的一个关键特点是，机器人执行的实验应该由任务的信息需求驱动。这项研究发展了(1)使用感知等价类的传感器解释和任务指导规划的理论，旨在应用于高度不确定或未建模的环境，例如移动机器人；(2)建模关于可识别性的几何约束的算法技术，并使用这些约束建立内部表示(例如地图)；(3)使用可识别集合的格子(信息层次)对任务的信息需求进行显式编码，这使得机器人能够执行实验来识别情况或地标；以及(4)使用由任务强加的几何约束、构造性可识别性实验和不确定性模型来合成健壮的移动机器人程序。本研究还将在实验室中实现该理论并在移动机器人上进行测试。

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.