A Caltech Library Service

Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions

Shi, Guanya and Hönig, Wolfgang and Shi, Xichen and Yue, Yisong and Chung, Soon-Jo (2022) Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions. IEEE Transactions on Robotics, 38 (2). pp. 1063-1079. ISSN 1552-3098. doi:10.1109/TRO.2021.3098436.

[img] PDF - Accepted Version
See Usage Policy.

[img] PDF - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper ItemVideo
Shi, Guanya0000-0002-9075-3705
Hönig, Wolfgang0000-0002-0773-028X
Shi, Xichen0000-0002-5366-9256
Yue, Yisong0000-0001-9127-1989
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 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.
Group:Center for Autonomous Systems and Technologies (CAST), GALCIT
Funding AgencyGrant Number
Center for Autonomous Systems and TechnologiesUNSPECIFIED
Raytheon CompanyUNSPECIFIED
Subject Keywords:Aerial systems, deep learning in robotics, multirobot motion planning and control, multirobot systems
Issue or Number:2
Record Number:CaltechAUTHORS:20210120-165259145
Persistent URL:
Official Citation:G. Shi, W. Hönig, X. Shi, Y. Yue and S. -J. Chung, "Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions," in IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1063-1079, April 2022, doi: 10.1109/TRO.2021.3098436
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107618
Deposited By: George Porter
Deposited On:21 Jan 2021 16:43
Last Modified:08 Apr 2022 17:52

Repository Staff Only: item control page