Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms

Abstract

This research explores a future concept requiring the efficient and safe, landing and recovery of a swarm of unmanned aerial vehicles (UAVs). The presented work involves the use of an overarching (centralized) airspace optimization model, formulated analytically as a network-based model with side constraints describing a time-expanded network model of the terminal airspace in which the UAVs navigate to one or more (possibly moving) landing zones. This model generates optimal paths in a centralized manner such that the UAVs are properly sequenced into the landing areas. The network-based model is "grown" using agent based simulation with simple flocking rules. The resulting solution is compared to another agent-based model which uses similar avoidance rules for the landing of these UAVs, exploring the benefit of distributed computation and decision-making characteristic of swarming models. Relevant measures of performance include, e.g., the total time necessary to land the swarm. Extensive simulation studies and sensitivity analyses are conducted to demonstrate the relative effectiveness of the proposed approaches.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA562712

Entities

People

  • Thomas F. Dono

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Electronic Warfare

DTIC Thesaurus Topics

  • Agent-Based Simulations
  • Air Traffic
  • Air Traffic Control Systems
  • Aircrafts
  • Algorithms
  • Collision Avoidance
  • Computational Science
  • Computer Programming
  • Control Systems
  • Ground Control Stations
  • Information Operations
  • Mathematical Models
  • Military Science
  • Operations Research
  • United States
  • Unmanned Aerial Vehicles
  • Unmanned Systems

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers