A Heuristic Method for Task Selection in Persistent ISR Missions Using Autonomous Unmanned Aerial Vehicles

Abstract

The Persistent Intelligence, Surveillance, and Reconnaissance (PISR) problem seeks to provide collection and delivery of data from prioritized ISR tasks using an autonomous Unmanned Aerial Vehicle (UAV). In this research, we investigate a method for selecting tasks called the Maximal Distance Discounted and Weighted Revisit Period (MD2WRP) utility function. We develop a two-step optimization method for the MD2WRP parameters for both single and multi-vehicle scenarios. We also compare the performance of MD2WRP to other common methods for PISR task selection. We find that the optimized MD2WRP function is competitive with these other methods. We also test MD2WRP under simulated operational constraints. For each constraint, we demonstrate how MD2WRP needs to be modified to compensate. Finally, we make practical suggestions about implementing MD2WRP, outline areas for future study, and offer recommendations about the conduct of PISR missions in general

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Document Details

Document Type
Technical Report
Publication Date
Sep 13, 2018
Accession Number
AD1123998

Entities

People

  • Christopher C. Olsen

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Autonomous Systems
  • Collision Avoidance
  • Control Systems
  • Department Of Defense
  • Engineering
  • Heuristic Methods
  • Information Science
  • Mathematical Programming
  • Operations Research
  • Systems Engineering
  • United States
  • Unmanned Aerial Vehicles
  • Unmanned Systems

Readers

  • Aviation Science / Aeronautics.
  • Computational Modeling and Simulation
  • Military Science and Technology Research and Modernization.

Technology Areas

  • Autonomy
  • Autonomy - UAVs