A Combat Simulation Analysis of Autonomous Legged Underwater Vehicles.

Abstract

Autonomous Legged Underwater Vehicles (ALUVs) are inexpensive crab-like robotic prototypes which will systematically hunt and neutralize mines en masse in the very shallow water and the surf zone (VSW/SZ). With the advent of mine proliferation and the focal shift of military power to the littorals of the world, ALUVs have the potential to fill a critical need of the United States Navy and Marine Corps mine countermeasure (MCM) forces. Duplicating the MCM portion of the Kernel Blitz 95 exercise whenever feasible, this thesis uses the Janus interactive combat wargaming simulation to model and evaluate the effectiveness of the ALUV as a MCM. Three scenarios were developed: an amphibious landing through a minefield using no clearing/breaching; an amphibious landing through a minefield using current clearing(breaching techniques; and an amphibious landing through a minefield using ALUVs as the clearing(breaching method. This thesis compares the three scenarios using landing force kills, cost analysis, combat power ashore, and percentage of mines neutralized as measures of effectiveness.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1996
Accession Number
ADA314862

Entities

People

  • Edwin E. Middlebrook

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • C4I
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Amphibious Operations
  • Cost Analysis
  • Databases
  • Geographic Regions
  • Landing Forces
  • Littoral Warfare
  • Marine Corps
  • Measures Of Effectiveness
  • Minefields
  • Operating Systems
  • Seabed
  • Simulations
  • Statistical Analysis
  • Underwater Vehicles
  • United States
  • Vehicles
  • Warfare

Readers

  • Acoustical Oceanography.
  • Naval Architecture and Marine Engineering.
  • Robotics and Automation.

Technology Areas

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