Platforms for Validation of Multi-Agent Autonomy

Abstract

We have developed a versatile, easy-to-use simulation engine that can be leveraged by the swarm research community to rapidly develop, evaluate, and adjust swarm algorithms for a variety of common benchmark scenarios. Our simulator is built on Microsoft AirSim and Unreal Engine, which provide support for vehicle and UAS models, together with a photorealistic graphics engine. We created an interface to allow swarm researchers to easily deploy and test their algorithms in complex simulated scenarios (such as search-and-rescue, ISR, and pursuit-evasion). This interface will allow researchers to choose the makeup of their swarm, and upload algorithms for different swarm tasks (such as patrolling, formation control, rendezvous, coverage, task allocation, etc.). The platform will maintain a library of benchmark scenarios, so that different algorithms can be easily and fairly evaluated against each other. We developed the simulator specifically to be deployed on the cloud, allowing ease of access to a wide class of researchers, obviating the need for expensive hardware and setup time, and allowing scaling to large swarms and environments. We took initial steps to enable the ability to test reinforcement learning algorithms for various swarm tasks such as formation control. We have also developed a multi-agent hardware testbed consisting of both unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) to combine their respective advantages (such as the large payload capacity of UGVs, and the maneuverability and speed of UAVs). The testbed leverages both low-cost quadrotors (AR Drones, Crazyflies, Mambo) and advanced autonomous vehicles (Jackal UGV) available at the PIs lab, which can work collaboratively through local communications and coordination. The testbed supports modularized sensors to enable more functionalities. Users can customize different sensors for a variety of tasks by simply plugging them into the expansion port of the deck.

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Document Details

Document Type
Technical Report
Publication Date
Oct 17, 2022
Accession Number
AD1212765

Entities

People

  • Shaoshuai Mou
  • Vinícius L. de Lima

Organizations

  • Purdue University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Autonomous Systems
  • Autonomous Vehicles
  • Closed Loop Systems
  • Computational Science
  • Control Systems
  • Equations
  • Ground Vehicles
  • Motion Capture
  • Multiagent Systems
  • Reinforcement Learning
  • Simulations
  • Simulators
  • Unmanned Aerial Vehicles
  • Unmanned Ground Vehicles
  • Unmanned Vehicles
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control