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Six units, from the physics of flight to the rules of the sky. Each ends in a build.
$135K+
Typical pay for a senior aerospace engineer in Seattle is $135K–$190K, per Glassdoor, Levels.fyi, Payscale and Indeed.
30K+
Trusted by 30,000+ Students. Join thousands who have chosen our programs.
144+
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Flying a vehicle is easy until something stops working: the GPS signal is jammed, the wind picks up, the link drops. This bootcamp is about the engineering that keeps working when the assumptions fail, from flight dynamics and control through state estimation and navigation without satellites, to autonomy and safe operation.
You start with the physics of how multirotors and fixed-wing aircraft fly, write a flight controller of your own, then work in the open-source PX4 stack and simulators. Navigation without GNSS is the centre of the programme: visual-inertial odometry, SLAM and the estimation theory underneath them.
It is written for engineers, pilots and developers who want to build autonomous vehicles, for robotics and software people moving into aerospace, and for anyone in the civil and defence drone sector who needs to understand the systems rather than only operate them.





Every unit runs in simulation first, with injected wind, sensor faults and signal loss, so failures happen on a screen and not in the air.
Each unit's work accumulates into projects you can run, show and explain, ending in a capstone flight system.
PX4, ROS 2, physics simulators and the leading open visual-inertial pipelines, the tools drone teams use in practice.
A full unit and a project on GNSS-denied navigation, the part of autonomy that matters most when conditions turn hostile.
The bootcamp sits beside the Academy's courses on drone flight, GNSS-denied autonomy and turbojet engineering, which you can take on their own for extra depth.
Round-the-clock help from the Academy's AI study assistants, for the mathematics, the code and the debugging.



Design the perception, planning and decision software that lets a vehicle operate without constant human control.
Design, tune and verify the control laws that keep a vehicle stable and responsive.
Build the estimation and localisation systems that tell a vehicle where it is, with or without satellites.
Work across aerodynamics, propulsion, systems and testing on aircraft and uncrewed vehicles.
Integrate airframe, sensors, compute, communications and software into a vehicle that works as a whole.
LUNARTECH AI stands as more than just another online education platform; it emerges as a pivotal beacon for those who have felt overlooked in the wave of technological advancement.

LunarTech is not just teaching data science; it's reshaping the very fabric of educational accessibility in the tech realm.
LunarTech’s solutions offer cutting-edge AI education, empowering individuals with in-demand skills to lead in tech.
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Forces and moments, coordinate frames, and the dynamics of multirotors and fixed-wing aircraft. You study lift, drag and thrust, why a banked turn behaves the way it does, and how propulsion shapes performance, then turn the models into a working simulation.








Cascaded PID control of attitude, rate and position, state-space design, and tuning against wind, payload changes and actuator limits. You write your own flight controller, then move to PX4: parameters, modes, logs and the failsafes that decide what a vehicle does when something goes wrong.








Inertial sensors, magnetometers, barometers and GNSS receivers, their error sources and their failure modes. You build Kalman and extended Kalman filters, fuse sensors, and learn how jamming and spoofing look in the data, and why an estimator needs to notice.








Visual-inertial odometry, SLAM, LiDAR odometry, terrain-relative and map-based localisation, and how to combine them into a navigation system that degrades gracefully. You study the leading open-source pipelines, benchmark them, and learn why they drift and what to do about it.








Path planning and obstacle avoidance in three dimensions, mission logic with state machines and behaviour trees, target detection and tracking with onboard neural networks, and coordination between vehicles. You work within real compute and power budgets, and learn where onboard AI helps and where it adds risk.








Airspace and the rules that govern it, with the EU open category as the worked example, operational risk assessment, command-and-control links, flight testing practice, maintenance and record-keeping. You also work through the ethics of dual-use systems, since the same autonomy stack serves survey, rescue and defence.





Master Autonomous Flight From the Mathematics Up with Our Autonomous Systems & Drones Bootcamp
Make vehicles that fly themselves, and keep flying when the satellites, the link or the plan goes away. Flight dynamics, control, estimation, navigation and autonomy.





























