Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

PDF Version Also Available for Download.

Description

In many situations, stand-off remote-sensing and hazard-interdiction techniques over realistic operational areas are often impractical "and difficult to characterize. An alternative approach is to implement an adap- tively deployable array of sensitive agent-specific devices. Our group has been studying the collective be- havior of an autonomous, multi-agent system applied to chedbio detection and related emerging threat applications, The current physics-based models we are using coordinate a sensor array for mukivanate sig- nal optimization and coverage as re,alized by a swarm of robots or mobile vehicles. These intelligent control systems integrate'glob"ally operating decision-making systems and locally cooperative learning neural net- works ... continued below

Creation Information

Asher, R.B.; Cameron, S.M.; Loubriel, G.M.; Robinett, R.D.; Stantz, K.M.; Trahan, M.W. et al. November 25, 1998.

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. More information about this article can be viewed below.

Who

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

Publisher

  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories, 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

In many situations, stand-off remote-sensing and hazard-interdiction techniques over realistic operational areas are often impractical "and difficult to characterize. An alternative approach is to implement an adap- tively deployable array of sensitive agent-specific devices. Our group has been studying the collective be- havior of an autonomous, multi-agent system applied to chedbio detection and related emerging threat applications, The current physics-based models we are using coordinate a sensor array for mukivanate sig- nal optimization and coverage as re,alized by a swarm of robots or mobile vehicles. These intelligent control systems integrate'glob"ally operating decision-making systems and locally cooperative learning neural net- works to enhance re+-timp operational responses to dynarnical environments examples of which include obstacle avoidance, res~onding to prevailing wind patterns, and overcoming other natural obscurants or in- terferences. Collectively',tkensor nefirons with simple properties, interacting according to basic community rules, can accomplish complex interconnecting functions such as generalization, error correction, pattern recognition, sensor fusion, and localization. Neural nets provide a greater degree of robusmess and fault tolerance than conventional systems in that minor variations or imperfections do not impair performance. The robotic platforms would be equipped with sensor devices that perform opticaI detection of biologicais in combination with multivariate chemical analysis tools based on genetic and neural network algorithms, laser-diode LIDAR analysis, ultra-wideband short-pulsed transmitting and receiving antennas, thermal im- a:ing sensors, and optical Communication technology providing robust data throughput pathways. Mission scenarios under consideration include ground penetrating radar (GPR) for detection of underground struc- tures, airborne systems, and plume migration and mitigation. We will describe our research in these areas anti give a status report on our progress.

Source

  • Fourth Joint Workshop on Standoff Detection for Chemical and Biological Defence; Williamsburg, VA; 10/26-30/1998

Language

Item Type

Identifier

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

  • Other: DE00002060
  • Report No.: SAND98-2652C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 2060
  • Archival Resource Key: ark:/67531/metadc666069

Collections

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

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • November 25, 1998

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

Description Last Updated

  • Dec. 6, 2016, 6:47 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 4

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Asher, R.B.; Cameron, S.M.; Loubriel, G.M.; Robinett, R.D.; Stantz, K.M.; Trahan, M.W. et al. Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents, article, November 25, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc666069/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.