Authors:

yahor lechanka

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About Paper:

With the growing development of advanced UAVs for air mobility and transportation, the demands for robustness and safety in autopilot firmware are increasing. To address these needs, a new autopilot system called CogniPilot is being developed at the Purdue UAS Research and Test Facility (PURT). CogniPilot leverages Lie Algebra and CasADi for implementing advanced control algorithms. This summer, Research and Development Drone 2 (RDD2) is being tested at PURT to validate real-world flight performance using estimation algorithms such as the attitude estimator. The development pipeline begins with a custom RViz-based simulation of RDD2, where all control algorithms are first validated. The system is then tested in Gazebo, which introduces more realistic physics and simulates RDD2's embedded hardware. Finally, the firmware is compiled for the NXP Real- Time Vehicle Management Unit and deployed on a physical quadrotor for indoor flight testing. The Qualisys Motion Capture System is used to track the drone's position and orientation, effectively emulating GPS indoors and providing ground truth for comparing against onboard estimates from the flight controller. The attitude estimator will utilize an Invariant Extended Kalman Filter. Additional modules-such as magnetometer attitude-free auto-calibration and a Wi-Fi-to-serial communication bridge-are also under development. The primary goal is to conduct successful test flights at PURT using the latest firmware, ensuring system stability. This framework will serve as a foundation for future contributors to develop their own modules and algorithms for machine learning, path planning, and more. Keywords: Autopilot; UAV

Source:

Purdue University / 2025

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Co-authors:

yahor lechanka