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Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions by Shi, Guanya; Hönig, Wolfgang; Shi, Xichen; Yue, Yisong; Chung, Soon-Jo is a scholarly article available to read on EtoBox.

What is Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions about?

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 (DNNs) with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) 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 multi-robot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neur

Author

Shi, Guanya; Hönig, Wolfgang; Shi, Xichen; Yue, Yisong; Chung, Soon-Jo

Published

2020

Language

EN