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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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This content will become publicly available on December 4, 2025
The OODA Loop of Cloudlet-Based Autonomous Drones
We present a benchmark-driven experimental study of autonomous drone agility relative to edge offload pipeline attributes. This pipeline includes a monocular gimbal-actuated on-drone camera, hardware RTSP video encoding, 4G LTE wireless network transmission, and computer vision processing on a ground-based GPU-equipped cloudlet. Our parameterized and reproducible agility benchmarks stress the OODA (“Observe, Orient, Decide, Act”) loop of the drone on obstacle avoidance and object tracking tasks. We characterize the latency and throughput of components of this OODA loop through software profiling, and identify opportunities for optimization.
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- Award ID(s):
- 2106862
- PAR ID:
- 10588994
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7828-3
- Page Range / eLocation ID:
- 178 to 190
- Subject(s) / Keyword(s):
- edge computing, machine learning, computer vision, drones, autonomous robotics, wireless networks, benchmarks, latency, throughput
- Format(s):
- Medium: X
- Location:
- Rome, Italy
- Sponsoring Org:
- National Science Foundation
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