Multi-Agent Control and Intelligent Sensor Allocation With Reinforcement Learning and Genetic Programming

Abstract

This project investigated the use of decentralized strategies for control of UAV and UGV swarms. During the first few months we developed an agent-based model of UAVs searching for targets in a pre-determined search area. We tested a variety of control and navigation strategies, including some based on biological principles. Subsequently we develop a second simulator, focusing on the problem of a team of pursuers trying to capture an evader in a 2-D urban environment. The simulator included control strategies for the evader and the pursuers, as well as an interactive world editor for creation of arbitrary urban environments. We used this simulator to run Monte Carlo simulations, obtaining some preliminary statistics on performance of the evader and pursuer strategies.

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Document Details

Document Type
Technical Report
Publication Date
Feb 03, 2003
Accession Number
ADA409970

Entities

People

  • Paolo Gaudiano

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Agent-Based Simulations
  • Algorithms
  • Collision Avoidance
  • Computer Programming
  • Computers
  • Contracts
  • Environment
  • Evolutionary Algorithms
  • Information Science
  • Line Of Sight
  • Machine Learning
  • Monte Carlo Method
  • Navigation
  • Personal Information Managers
  • Reinforcement Learning
  • Simulations
  • Simulators

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Game Theory.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Biotechnology