Mixed Reality Experimental Pattern Formation of Communicating Unmanned Vehicles - A Joint NRL/UPENN experiment

Abstract

Unmanned aerial vehicles (UAVs) are becoming vital warfare and homeland-security platforms because they significantly reduce costs and the risk to human life while amplifying war-?fighter and ?first-responder capabilities. These vehicles have been used in Iraq and during Hurricane Katrina rescue e -orts with some success, but there remains a formidable barrier to achieving the vision of multipe UAVs operating cooperative lyas a swarm. It is necessary to consider general forms of network communication and coupling mechanisms between agents in order to model collective motion patterns of interacting autonomous vehicles. The purpose of this proposal is to reproduce experimentally and explore self-organized states predicted theoretically in a system of communicating robots in the presence of uncertainties and delays in communication. In doing so, we will consider motion patterns of agents where communication between networked agents is delayed and asynchronous, and the agents have heterogeneous dynamical characteristics. Noise and uncertainty will be included so that pattern stability may be quantified in terms of vehicle characteristics as well as environmental characteristics. Common characteristics across platforms, as well as differences in dynamics, will be tested across two distinct platforms: NRL s autonomous air vehicles, and UPENN s micro-Autonomous Surface Vehicles on water.

Document Details

Document Type
DoD Grant Award
Publication Date
Jul 26, 2018
Source ID
N000141812580

Entities

People

  • Mong-ying Hsieh

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Pennsylvania

Tags

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - UAVs