VisTune: Auto-Tuner for UAVs Using Vision-Based Localization

Muhammad Ahmed Humais, Mohammad Chehadeh, Rana Azzam, Igor Boiko, Yahya H. Zweiri

Abstract: This letter presents VisTune, a method for automatic controller tuning specifically designed for UAVs using vision-based localization (VBL) for position control. In contrast to existing methods that involve manually flying the UAV to collect data for system identification and tuning, our approach leverages relay-based system identification and tuning, which autonomously generates stable oscillations without the need for a stabilizing controller. The entire process concludes within a few seconds. Prior work in vision-based position control of the UAVs often ignores the delay from the perception pipeline, which is quite significant and results in suboptimal tuning and poor control performance. Our approach accounts for perception delay and addresses practical issues, such as varying delays due to varying computation requirements and inevitable estimation errors, which pose challenges in applying relay-based identification and tuning. Typically, VBL system introduces over 100ms of delay, compared to less than 20ms delay when motion capture system is used. Moreover, we show that the perception delay identified by VisTune can be effectively used to temporally advance the feedforward acceleration signal to achieve better tracking performance. Finally, we demonstrate the robustness of the tuned controllers on a trajectory tracking task, reaching speeds of up to 2.1m/s with an RMS control error of only 0.054m. Under wind disturbance of 5m/s, we report an RMSE of 0.116m. A video of the experiments is available at https://youtu.be/hJoT8bn0K0o .