Towards Task-Level Planning: Action-Based Sensor Design

迈向任务级规划：基于动作的传感器设计

• 批准号: 9213993
• 负责人: Michael Erdmann
• 金额: $ 21.5万
• 依托单位: Carnegie-Mellon 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 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9213993&HistoricalAwards=false
• 关键词: Towards; Task; Level; Planning; Action

This is the first year of a three-year continuing award. The research explores the information requirements of robot tasks, for automating design of sensors from the specification of a robot's task, its actions, and its uncertainty in sensing and control. The sensors provide precisely the information required by the robot to perform its task, despite the sensing and control uncertainties. The key idea is to generate a strategy for the robot task by using a backchaining planner that assumes perfect sensing while taking careful account of control uncertainty. The resulting plan indirectly specifies a sensor that tells the robot when to execute which action. The sensor need not provide perfect information, but only that information required for the plan to function correctly. In contrast with traditional approaches to task planning which use a bottom-up design that focuses on the sensor characteristics and accommodates them, this approach is driven from the top, by questions such as "given a task, what sensor characteristics and tolerances are needed," and "how does sensing depend on action." The research investigates special purpose sensors for several simple assembly tasks, including multiple-peg-in-hole assembly, parts identification, parts orienting, and sorting. The ultimate goal is to develop task-level planners that can translate high-level task specifications into low-level robot commands.

今年是连续三年获奖的第一年。 的 研究探索了机器人任务的信息需求， 自动化设计的传感器从一个机器人的规格 任务，它的行动，以及它在感知和控制方面的不确定性。 的 传感器精确地提供机器人所需的信息， 执行其任务，尽管传感和控制的不确定性。 其关键思想是通过使用 一个后链规划器，假设完美的感觉，同时采取 仔细考虑控制不确定性。 由此产生的计划 间接指定一个传感器，告诉机器人何时执行 什么行动。 传感器不需要提供完美的信息，但 只有计划正确运行所需的信息。 与传统的任务规划方法相比， 自下而上的设计，专注于传感器特性， 适应他们，这种方法是从顶部驱动， 诸如“给定任务，什么传感器特性， 公差是需要的”，以及“传感如何取决于行动。" 该研究调查了几种特殊用途的传感器 简单的装配任务，包括多钉孔装配， 零件识别、零件定向和分类。 最终 我们的目标是开发任务级别的计划者， 高级任务规范转换为低级机器人命令。