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About this paper
Sindre M. Hegre, Welf Rehberg, Mihir Kulkarni, Kostas Alexis
Researchers from the Norwegian University of Science and Technology developed an open-source framework enabling direct deployment of neural network controllers on standard PX4-based drone flight controllers. This solution allows neural controllers to run at 650 Hz on resource-constrained embedded systems like the mRobotics Pixracer Pro, achieving real-world position tracking comparable to traditional methods without requiring additional companion computers.
Key insights
- Neural network controllers can operate efficiently and in real-time directly on resource-constrained embedded flight controllers like the Pixracer Pro.
- Careful system identification, domain randomization during training, and robust reward design are essential for achieving successful sim2real transfer of learning-based aerial vehicle controllers.
- Integrating an end-to-end neural network that directly maps setpoints to motor commands can reduce complexity and computational overhead compared to cascaded control pipelines.
Summary by alphaXiv.
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