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Unified Co-Simulation Framework for Autonomous UAVs

Published: 10 September 2023 Publication History

Abstract

Autonomous drones (UAVs) have rapidly grown in popularity due to their form factor, agility, and ability to operate in harsh or hostile environments. Drone systems come in various form factors and configurations and operate under tight physical parameters. Further, it has been a significant challenge for architects and researchers to develop optimal drone designs as open-source simulation frameworks either lack the necessary capabilities to simulate a full drone flight stack or they are extremely tedious to setup with little or no maintenance or support. In this paper, we develop and present UniUAVSim, our fully open-source co-simulation framework capable of running software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations concurrently. The paper also provides insights into the abstraction of a drone flight stack and details how these abstractions aid in creating a simulation framework which can accurately provide an optimal drone design given physical parameters and constraints. The framework was validated with real-world hardware and is available to the research community to aid in future architecture research for autonomous systems.

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cover image ACM Conferences
PEARC '23: Practice and Experience in Advanced Research Computing 2023: Computing for the Common Good
July 2023
519 pages
ISBN:9781450399852
DOI:10.1145/3569951
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 10 September 2023

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Author Tags

  1. Distributed Systems
  2. Drones
  3. FPGA
  4. Hardware-in-the-loop
  5. Simulation
  6. Software-in-the-loop
  7. UAV

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Overall Acceptance Rate 133 of 202 submissions, 66%

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