Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions
We present Neural-Swarm2 , a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned deep neural networks with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: 1) Spectral normalization for stability and generalization guarantees of unseen data and 2) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multirobot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.
© 2021 IEEE. Manuscript received November 24, 2020; revised May 16, 2021; accepted June 15, 2021. Date of publication August 6, 2021; date of current version April 5, 2022. The work was supported in part by Caltech's Center for Autonomous Systems and Technologies (CAST), the Raytheon Company, and the Jet Propulsion Laboratory. This paper was recommended for publication by Associate Editor M. Schwager and Editor P. R. Giordano upon evaluation of the reviewers' comments.
Submitted - 2012.05457.pdf