Market-Driven Multi-Robot Exploration

Abstract

For many real-world applications, autonomous robots must execute complex tasks in unknown or partially known unstructured environments. This work presents a novel approach to efficient multi-robot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are also given.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA529530

Entities

People

  • Anthony Stentz
  • M. B. Dias
  • Robert Zlot
  • Scott Thayer

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Dead Reckoning
  • Demographic Cohorts
  • Detectors
  • Efficiency
  • Environment
  • Extreme Environments
  • Information Exchange
  • Market Economy
  • Markets
  • Motion Planning
  • Negotiations
  • Numbers
  • Real Numbers
  • Robotic Swarms
  • Robotics
  • Robots

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Economics
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control