Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

PDF Version Also Available for Download.

Description

This paper focuses on our recent work at Sandia National Laboratories toward engineering a physics-based swarm of mobile vehicles for distributed sensing applications. Our goal is to coordinate a sensor array that optimizes sensor coverage and multivariate signal analysis by implementing artificial intelligence and evolutionary computational techniques. These intelligent control systems integrate both globally operating decision-making systems and locally cooperative information-sharing modes using genetically-trained neural networks. Once trained, neural networks have the ability to enhance real-time operational responses to dynamical environments, such as obstacle avoidance, responding to prevailing wind patterns, and overcoming other natural obscurants or interferences (jammers). The swarm ... continued below

Physical Description

18 p.

Creation Information

Cameron, S.M.; Loubriel, G.M.; Rbinett, R.D. III; Stantz, K.M.; Trahan, M.W. & Wagner, J.S. April 1, 1999.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 37 times . More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Sponsor

Publisher

  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA
    Place of Publication: Albuquerque, New Mexico

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

This paper focuses on our recent work at Sandia National Laboratories toward engineering a physics-based swarm of mobile vehicles for distributed sensing applications. Our goal is to coordinate a sensor array that optimizes sensor coverage and multivariate signal analysis by implementing artificial intelligence and evolutionary computational techniques. These intelligent control systems integrate both globally operating decision-making systems and locally cooperative information-sharing modes using genetically-trained neural networks. Once trained, neural networks have the ability to enhance real-time operational responses to dynamical environments, such as obstacle avoidance, responding to prevailing wind patterns, and overcoming other natural obscurants or interferences (jammers). The swarm realizes a collective set of sensor neurons with simple properties incorporating interactions based on basic community rules (potential fields) and complex interconnecting functions based on various neural network architectures, Therefore, the swarm is capable of redundant heterogeneous measurements which furnishes an additional degree of robustness and fault tolerance not afforded by conventional systems, while accomplishing such cognitive tasks as generalization, error correction, pattern recognition, and sensor fission. The robotic platforms could be equipped with specialized sensor devices including transmit/receive dipole antennas, chemical or biological sniffers in combination with recognition analysis tools, communication modulators, and laser diodes. Our group has been studying the collective behavior of an autonomous, multi-agent system applied to emerging threat applications. To accomplish such tasks, research in the fields of robotics, sensor technology, and swarms are being conducted within an integrated program. Mission scenarios under consideration include ground penetrating impulse radar (GPR) for detection of under-ground structures, airborne systems, and plume detection/remediation. We will describe our research in these areas and give a status report on our progress, including simulations and laboratory-based sensor experiments.

Physical Description

18 p.

Notes

OSTI as DE00005682

Medium: P; Size: 18 pages

Source

  • SPIE 13th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls, Orlando, FL (US), 04/05/1999--04/09/1999

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Report No.: SAND99-0770C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 5682
  • Archival Resource Key: ark:/67531/metadc694328

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • April 1, 1999

Added to The UNT Digital Library

  • Aug. 14, 2015, 8:43 a.m.

Description Last Updated

  • April 7, 2017, 4:04 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 37

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Cameron, S.M.; Loubriel, G.M.; Rbinett, R.D. III; Stantz, K.M.; Trahan, M.W. & Wagner, J.S. Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents, article, April 1, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc694328/: accessed August 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.