Towards Task-Level Planning: Action-Based Sensor Design

Abstract

This research proposes a method for automatically designing sensors from the specifications 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.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1992
Accession Number
ADA255266

Entities

People

  • Michael Erdmann

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Autonomous Systems
  • Collision Avoidance
  • Computer Science
  • Control Systems
  • Coordinate Systems
  • Geometry
  • Mechanical Engineering
  • Mechanics
  • Motion Planning
  • Operations Research
  • Robot Navigation
  • Robotics
  • Robots
  • Specifications

Fields of Study

  • Computer science

Readers

  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy