Urban Fasel

Lecturer at Imperial College London

Flapping wing micro aerial vehicles

Accurate and agile trajectory tracking in subgram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust feedback controllers, while the fast dynamics and computational constraints prevent the deployment of computationally expensive strategies. In this work, we present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly [1], a subgram MAV (0.7 grams). Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC). The neural network policy is obtained using our recent work [2], which enables the policy to preserve the robustness of RTMPC, but at a fraction of its computational cost. We experimentally evaluate our approach, achieving position Root Mean Square Errors (RMSEs) lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to [3], and demonstrating robustness to large external disturbances. 

Figure:  Composite image showing a 7.5-second flight where the MIT SoftFly [1], a soft-actuated, insect-scale MAV, follows a vertical circle with 5 cm radius. The robot is controlled by a neural network policy, trained to reproduce the response of a robust model predictive controller. Thanks to its computational efficiency, the neural network controls the robot at 2 kHz while running on a small offboard computer.
[1] Y. Chen, H. Zhao, J. Mao, P. Chirarattananon, E. F. Helbling, N.-s. P. Hyun, D. R. Clarke, and R. J. Wood, “Controlled flight of a microrobot powered by soft artificial muscles,” Nature, vol. 575, no. 7782, pp. 324–329, 2019. 
[2] A. Tagliabue, D.-K. Kim, M. Everett, and J. P. How, “Demonstrationefficient guided policy search via imitation of robust tube MPC,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 462–468. 
[3] Y. Chen, S. Xu, Z. Ren, and P. Chirarattananon, “Collision resilient insect-scale soft-actuated aerial robots with high agility,” IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1752–1764, 2021. 


Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC

Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How

Submitted to ICRA 2023


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