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sUAS Edge AI & Autonomous Systems — Engineering Masterclass

An interactive, self-paced study guide for engineers building AI-powered small Unmanned Aircraft Systems (sUAS). The curriculum covers the full stack: from the physics of flight and embedded silicon, through perception and sensor fusion, to swarm coordination and counter-UAS security.

Both sides of the field are covered — commercial and industrial operations (inspection, delivery, survey, public safety, and the Part 108 BVLOS transition) alongside contested-environment and defense engineering (EW resilience, GNSS-denied navigation, Blue UAS compliance).

Live site: https://romankalinchuk.github.io/sUAS-AI-StudyGuide

Content currency: technical claims, versions, and regulatory status were last reviewed against primary sources in August 2026. Fast-moving areas — FAA Part 108 rulemaking, FCC Covered List exemptions, the Blue UAS Cleared List, and NVIDIA/ROS release cadence — should be re-verified before you rely on them.


What's inside

17 modules:

# Module Topics
1 Fundamentals & Autonomy Autonomy levels, OODA loop, edge vs. cloud AI, Part 107/108, Blue UAS, commercial market
2 SWaP-C Physics & Math Momentum theory, blade element theory, disk loading, thermal design, endurance math
3 Power Electronics & Circuits Battery chemistry, semi-solid-state cells, ESC firmware, BECs, power distribution
4 Compute Silicon Matrix Jetson Orin Super & Thor, Hailo, RK3588, flight controllers, cameras, LiDAR, GNSS
5 Flight Controller Architecture Cascaded PID, EKF, flight modes, MAVLink/uXRCE-DDS, PX4 vs. ArduPilot
6 RF Communications & Link Mgmt Link budgets, ExpressLRS, FHSS/DSSS, fiber-optic control links, mesh, SDR
7 Edge Software Toolchains TensorRT, ONNX Runtime, ROS 2 LTS selection, Isaac ROS, containers, real-time Linux
8 Data Links & Topology MIPI CSI-2, CAN/DroneCAN, video pipelines, time sync, bandwidth budgeting
9 Sensor Fusion & EKF EKF derivation, UKF and ESKF variants, EKF3 state vector, delayed-measurement handling
10 AI Training & Dataset Pipeline Dataset curation, transfer learning, YOLO26, quantization, TensorRT engines
11 Perception & VSLAM Detection, tracking, thermal IR, camera geometry, VIO, geometric foundation models
12 Depth Sensing & 3D Mapping Stereo, ToF, LiDAR, FAST-LIO2, nvblox, 3D Gaussian Splatting
13 Path Planning & Navigation A*, D* Lite, SMAC, MPPI, minimum-snap trajectories, GNSS-denied fallback, RTK
14 AI Targeting & Kinematics Object tracking, pursuit kinematics, gimbal control, human-in-the-loop constraints
15 Swarm Intelligence Boids, consensus protocols, task allocation, mesh coordination, program realities
16 Security & Counter-UAS MAVLink signing, GPS spoofing, secure boot, adversarial ML, C-UAS, NDAA/FCC compliance
17 Implementation Workflow End-to-end build checklist, integration steps

Interactive elements

  • Thermal estimator — models case temperature vs. processor power, ambient, and airflow, against the 80 °C Jetson case limit (Module 2)
  • Hardware comparison chart — bubble chart of AI performance vs. cost vs. power across GPU/NPU/SoC/FPGA options (Module 4)
  • Data bandwidth calculator — raw CSI-2 sensor load and compressed downlink bitrate, with interface recommendations (Module 8)
  • Live Boids swarm simulation — tune separation, alignment, and cohesion in real time (Module 15)
  • Implementation workflow stepper — guided build process with expandable steps (Module 17)

Running locally

The site uses ES modules, so it requires an HTTP server (browsers block file:// imports). Any static server pointed at the public/ directory works:

# Python (no install needed)
cd public && python3 -m http.server 8080

# Node.js npx
npx serve public

Then open http://localhost:8080 in your browser.

There is no build step, no bundler, and no package.json. Third-party libraries (Chart.js, Prism, KaTeX) load from CDNs at runtime, so a network connection is needed for syntax highlighting, the hardware chart, and math rendering.


Deployment

Pushing to main automatically deploys the public/ directory to GitHub Pages via .github/workflows/deploy.yml.

To enable Pages on a fresh fork:

  1. Go to Settings → Pages
  2. Set Source to GitHub Actions

Contributing corrections

This material makes a lot of specific, checkable claims — part counts, power figures, release dates, regulatory deadlines. If you find one that is wrong or has gone stale, corrections are welcome. Claims sourced to a vendor datasheet, a standards document, or a primary regulatory filing are preferred over secondary reporting.

About

Study guide for implementing AI on sUAS systems. Each module goes into detail about specific topics to help guide learning and studying. Modules will be updated to stay current.

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