Module 1
UAV Fundamentals and System Architecture
Understand the complete UAV as an integrated robotic system.
Learning track
UAV Systems Engineering & Autonomous Navigation
From UAV fundamentals to GNSS-denied autonomous flight.
129 of 129 lessons are written and open; the rest are listed as planned and are not yet available.
Why this track exists
Most drone tutorials stop at making a quadrotor hover. This track treats the UAV as an integrated robotic system: airframe, sensors, estimator, planner, controller, flight software, onboard compute, timing, and the safety case that decides whether it may fly. It is aimed at robotics students, UAV and GNC engineers, perception and sensor-fusion engineers, and researchers working on aerial autonomy.
The material is written for engineers: equations with the symbols defined, worked numbers, failure modes and debugging steps for every lesson, and primary references. Each lesson is labelled with how settled its content is, so you can tell textbook principle from a tool-specific detail or a research result.
What you will build
- A 6-DoF quadrotor model with motor mixing and cascaded attitude control.
- A visual odometry pipeline and an IMU-aided estimator, evaluated against ground truth.
- An EKF you can tune and inspect step by step, with innovation-based fault detection.
- An MPC trajectory tracker with velocity and acceleration constraints.
- A PX4 SITL + ROS 2 offboard autonomy stack with external odometry.
- A complete GNSS-denied UAV design package: requirements, ICD, FMEA, test and acceptance plans.
Learning outcomes
- Model and simulate UAV dynamics and choose between PID, LQR and MPC.
- Explain why GNSS fails and design a layered navigation fallback.
- Build and validate estimators; judge them with NIS and NEES.
- Integrate autonomy software with PX4 safely.
- Budget compute, power, latency and bandwidth for onboard hardware.
- Write verifiable requirements and a flight-test acceptance plan.
How to read the labels
- Established engineering Textbook or standards-level material that is stable across implementations.
- Implementation-specific Behaviour of a particular tool or version (PX4, ROS 2, Jetson). Check the linked documentation for your version.
- Experimental Practices that work in published demonstrations but are not yet routine or standardised.
- Research-level Methods from recent papers; results depend on the dataset and are not guarantees.
Eight modules
Module 2
UAV Navigation and GNSS-Denied Operations
Understand how UAVs navigate without reliable GNSS signals.
Module 3
Sensor Fusion and State Estimation
Develop reliable state estimation for autonomous UAVs.
Module 4
SLAM and Autonomous Localization
Enable UAVs to localize themselves and construct maps.
Module 5
UAV Guidance, Navigation and Control (GNC)
Understand how a UAV maintains stability and follows trajectories.
Module 6
UAV Software and Flight-Controller Integration
Integrate robotics autonomy software with real UAV flight controllers.
Module 7
Embedded Systems, Timing and System Integration
Build an integrated autonomous system that executes reliably on onboard hardware.
Module 8
Safety, Verification and Flight Testing
Validate complete autonomous UAV systems.
Interactive engineering simulations
Seven browser simulations. They run entirely in JavaScript on your device, with no server.
M1-L1
Quadrotor Dynamics Simulator
Change mass, thrust, motor lag, PID gains and initial attitude, and watch a 6-DoF quadrotor recover.
M2-L3
Visual Odometry Simulator
Two synthetic camera frames, detected corners, matches, optical flow, the estimated motion, and what happens when tracking fails.
M3-L4
Sensor Fusion Simulator
IMU-only, visual odometry, VIO and LiDAR-IMU estimators against ground truth: trajectory, error, covariance and drift.
M3-L2
EKF Simulator
Tune process noise, measurement noise, initial covariance and update rate, and see predict and correct steps one at a time.
M5-L3
MPC Flight Controller Simulator
Tune prediction horizon, control horizon, velocity and acceleration limits, and trajectory; see tracking and the control response.
M8-L2
GNSS-Denied Failure Simulator
Inject GNSS loss, spoofing, camera failure, low features, IMU bias, LiDAR failure and link loss; watch the navigation architecture degrade.
M1-L6
UAV System Architecture Builder
Wire Sensors, Perception, State Estimation, Planning and Flight Control together, and inspect every interface and data flow.
M5-L6
3-D Voxel Path Planner
Plan through a 3-D voxel map with A*: change the neighbours, heuristic, weight and vehicle size, and watch the search, the path and the shortcut.
Engineering calculators
Thrust-to-weight, endurance, motor mixer and frame conversion, with the formulas shown. Open the calculators.
Capstone projects
Capstone 1
Vision-Aided Autonomous UAV
Full brief, requirements, deliverables and rubric.
Capstone 2
LiDAR-Inertial GNSS-Denied UAV
Full brief, requirements, deliverables and rubric.
Capstone 3
ROS 2 + PX4 Autonomous UAV
Full brief, requirements, deliverables and rubric.
Capstone 4
Complete GNSS-Denied Autonomous UAV
Full brief, requirements, deliverables and rubric.
Also in the track
- Interview preparation: every lesson’s interview questions in one place.
- References and documentation: the primary sources behind the lessons.
Get notified when new UAV lessons and labs are released.
One email per release.