Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRecalde, Luis-
dc.contributor.authorGuevara, Bryan-
dc.contributor.authorAndaluz, Víctor-
dc.contributor.authorVarela-Aldás, José-
dc.contributor.authorGimenez, Javier-
dc.contributor.authorGandolfo, Daniel-
dc.description.abstractAccurate trajectory tracking for multiple quadrotors is essential for safe navigation in dynamic environments. However, this can be a challenge due to high nonlinearities of the systems, environment's obstacles and constraints in control actions. This article presents a nonlinear model predictive control (NMPC) for tracking a variety of trajectories using multiple quadrotors in a unknown environment with dynamic and static obstacles. The controller is formulated using kinematic and dynamic models of multiple commercial aerial robots (Mavic Pro 2), actuators limitations, and finally converting obstacles objects to states constraints. It was developed through CasADI, an open-source software for numerical optimization that provides a fast solution to keep each quadrotor on the desired trajectory while nonlinear constraints guarantee collision avoidance and smooth control signals. The controller is implemented in simulations to systematically evaluate the performance, considering tracking accuracy and computational time. The results of the simulations are presented to confirm that the proposed NMPC generates a smooth control values ensuring safe states for each aerial robot while following a desired trajectory in unknown
dc.publisher2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022. Pages 1201 - 1209. Dubrovnik. 21 June 2022 through 24 June 2022es
dc.titleNonlinear MPC for Multiple Quadrotors in Dynamic Environmentses
Appears in Collections:Artículos Científicos Indexados

Files in This Item:
There are no files associated with this item.

This item is licensed under a Creative Commons License Creative Commons