Desenvolvimento de controle autônomo para execução de manobras acrobáticas em drones Crazyflie utilizando processos gaussianos

Abstract

The present work addresses the development of an autonomous control strategy for executing complex aerobatic maneuvers in nano drones, specifically the full backward rotation maneuver in the Crazyflie model. The main objective of the study consists of reproducing and validating a Bayesian optimization algorithm, using Gaussian process surrogate models, to plan and execute the aerobatic maneuver in a high-fidelity simulation environment, employing classic controllers only for basic flight stabilization. The methodology adopts a machine learning approach where the maneuver is segmented into five sequential dynamic phases of motor actuation. The virtual environment allows the algorithm to explore and learn aerodynamic uncertainties through iterative attempts. The system autonomously adjusts actuation times and motor thrust based on a cost function that evaluates spatial precision and recovery stability, comparing the findings with an analytical mathematical model solved separately. The results demonstrate the successful convergence of the optimization algorithm after seven thousand flight iterations, identifying the exact parameters for each stage of the movement. The simulation proves that the vehicle executes the three hundred and sixty-degree rotation contained within the established spatial constraints, compensating for lift losses and chaotic turbulence without the need for exhaustive mathematical modeling of drag forces. Immediately after the rotation, the stabilizing control successfully resumes mechanical authority, dampening the free fall. It is concluded that artificial intelligence, based on statistical probabilistic methods, overcomes the physical limitations of traditional linear models in aggressive flight scenarios. The developed hybrid control architecture proves to be safe, robust, and effective, fully validating the feasibility of subsequently transferring the optimized mathematical parameters for testing on the physical hardware of the aircraft.