Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: This research proposes a method for automatically designing 
        sensors from the specification of a robot's task, its actions, 
        and its uncertainty in control.
        The sensors provide precisely the information required by the
        robot to perform its task, despite uncertainty in sensing and
        control.
        The key idea is to generate a strategy for a 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.
        Although the planner assumes perfect sensing information, the 
        sensor need not actually provide perfect information.
        Instead, the sensor provides only the information required for 
        the plan to function correctly.
        
        This report is a revised version of a proposal currently 
        submitted to NSF.
Number: CMU-CS-92-116
Bibtype: TechReport
Month: feb
Author: Michael Erdmann
Title: Towards Task-Level Planning:
        Action-Based Sensor Design 
Year: 1992
Address: Pittsburgh, PA
Super: @CMUTR