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.
Authors
- Jingao Xu (ORCID: https://orcid.org/0000-0002-8347-2657)
- Mahadev Satyanarayanan (ORCID: https://orcid.org/0000-0002-2187-2049)
- Thomas Eiszler (ORCID: https://orcid.org/0000-0003-3400-2947)
- Xiangliang Chen (ORCID: https://orcid.org/0009-0009-4720-6122)
- Padmanabhan Pillai (ORCID: https://orcid.org/0000-0003-0171-020X)
- Qifei Dong (ORCID: https://orcid.org/0000-0001-6300-8796)
- Mihir Bala (ORCID: https://orcid.org/0009-0004-0847-8946)
- Aditya Chanana
Institutions
- Intel (United States) (US)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1109/sec62691.2024.00022
- Citations
- 1
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Science Foundation