Salvato in:
Dettagli Bibliografici
Autori principali: Bueno, Vitor, Azarbahram, Ali, Farina, Marcello, Fagiano, Lorenzo
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:https://arxiv.org/abs/2511.06990
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866914146745843712
author Bueno, Vitor
Azarbahram, Ali
Farina, Marcello
Fagiano, Lorenzo
author_facet Bueno, Vitor
Azarbahram, Ali
Farina, Marcello
Fagiano, Lorenzo
contents This paper presents a Koopman-based model predictive control (MPC) framework for safe UAV navigation in dynamic environments using real-time LiDAR data. By leveraging the Koopman operator to linearly approximate the dynamics of surrounding objets, we enable efficient and accurate prediction of the position of moving obstacles. Embedding this into an MPC formulation ensures robust, collision-free trajectory planning suitable for real-time execution. The method is validated through simulation and ROS2-Gazebo implementation, demonstrating reliable performance under sensor noise, actuation delays, and environmental uncertainty.
format Preprint
id arxiv_https___arxiv_org_abs_2511_06990
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Koopman-Based Dynamic Environment Prediction for Safe UAV Navigation
Bueno, Vitor
Azarbahram, Ali
Farina, Marcello
Fagiano, Lorenzo
Systems and Control
This paper presents a Koopman-based model predictive control (MPC) framework for safe UAV navigation in dynamic environments using real-time LiDAR data. By leveraging the Koopman operator to linearly approximate the dynamics of surrounding objets, we enable efficient and accurate prediction of the position of moving obstacles. Embedding this into an MPC formulation ensures robust, collision-free trajectory planning suitable for real-time execution. The method is validated through simulation and ROS2-Gazebo implementation, demonstrating reliable performance under sensor noise, actuation delays, and environmental uncertainty.
title Koopman-Based Dynamic Environment Prediction for Safe UAV Navigation
topic Systems and Control
url https://arxiv.org/abs/2511.06990