Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot

Unmanned aerial vehicles have advanced quickly and are now a dominant force in several domains, including military, security, and even logistics. 3D simulations with virtual environments are cost effective tools for assessing feasibility and costs of developing new systems or upgrading current systems. This is especially true for multiagent or swarm systems that require many agents working in concert. The total costs can be much higher when only physical prototypes are used for testing and analysis. There are existing 3D libraries for aiding with UAV simulations, but they’re too specialized; it is difficult to create highly customized virtual environments with these packages. A simulation development framework needs to be capable and stable for accurate simulations yet generalized enough that any types of simulations can be developed without exhausting efforts. This paper explores the feasibility and ease of using Godot, an open-source game engine, to develop a simulation that can assess the function of a multiagent UAV system. It is found that the Godot engine is a powerful tool that is both accurate and generalized enough to develop highly customizable UAV simulation. This allows verification and testing of UAV designs and control algorithms in highly customizable and flexible virtual environments, using a free open-sourced tool.

Authors

Institutions

Publication Details

Published
2024-12-02
DOI
https://doi.org/10.1109/swc62898.2024.00346
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot

Leo Howard, Fuhua Lin, Henry Leung
Reinforcement Learning in Robotics
article

Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot

Leo Howard, Fuhua Lin, Henry Leung
article en

Abstract

Unmanned aerial vehicles have advanced quickly and are now a dominant force in several domains, including military, security, and even logistics. 3D simulations with virtual environments are cost effective tools for assessing feasibility and costs of developing new systems or upgrading current systems. This is especially true for multiagent or swarm systems that require many agents working in concert. The total costs can be much higher when only physical prototypes are used for testing and analysis. There are existing 3D libraries for aiding with UAV simulations, but they’re too specialized; it is difficult to create highly customized virtual environments with these packages. A simulation development framework needs to be capable and stable for accurate simulations yet generalized enough that any types of simulations can be developed without exhausting efforts. This paper explores the feasibility and ease of using Godot, an open-source game engine, to develop a simulation that can assess the function of a multiagent UAV system. It is found that the Godot engine is a powerful tool that is both accurate and generalized enough to develop highly customizable UAV simulation. This allows verification and testing of UAV designs and control algorithms in highly customizable and flexible virtual environments, using a free open-sourced tool.

University of Calgary (CA), Athabasca University (CA)
Affordable and clean energy
Openalex Percentile: Top 74%
Reinforcement Learning in Robotics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot — Leo Howard, Fuhua Lin, et al. · (2024) | TGRS Research Map | TGRS