Publication result detail

Surrogate Modeling of Optimal Control Based Collision Avoidance System for Multirotor Unmanned Aerial Vehicles

NOVÁK, J.; CHUDÝ, P.

Original Title

Surrogate Modeling of Optimal Control Based Collision Avoidance System for Multirotor Unmanned Aerial Vehicles

English Title

Surrogate Modeling of Optimal Control Based Collision Avoidance System for Multirotor Unmanned Aerial Vehicles

Type

Paper in proceedings (conference paper)

Original Abstract

A dynamically changing operating environment, along with constraints imposed through applicable safety requirements, pose significant challenges to autonomous multi-rotor manned and unmanned aerial vehicle operations in urban areas. Safety-critical onboard collision avoidance capability requires fast decision making accounting for uncertainties arising in complex environments. Successive convexification approach is applied to generate collision avoidance trajectories assuming both static and moving obstacles. The uncertainties arising in estimated state of moving obstacles are accounted for by construction of Polynomial Chaos Expansion based surrogate model. The obtained surrogate model can be evaluated in real-time to update the collision avoidance trajectory in case of change of tracked obstacle's state. The designed trajectories are subsequently tracked using a closed-loop Model Predictive Control scheme assuming a quadcopter configuration.

English abstract

A dynamically changing operating environment, along with constraints imposed through applicable safety requirements, pose significant challenges to autonomous multi-rotor manned and unmanned aerial vehicle operations in urban areas. Safety-critical onboard collision avoidance capability requires fast decision making accounting for uncertainties arising in complex environments. Successive convexification approach is applied to generate collision avoidance trajectories assuming both static and moving obstacles. The uncertainties arising in estimated state of moving obstacles are accounted for by construction of Polynomial Chaos Expansion based surrogate model. The obtained surrogate model can be evaluated in real-time to update the collision avoidance trajectory in case of change of tracked obstacle's state. The designed trajectories are subsequently tracked using a closed-loop Model Predictive Control scheme assuming a quadcopter configuration.

Keywords

collision avoidance, polynomial chaos expansion,
multi-rotor vehicle, successive convexification

Key words in English

collision avoidance, polynomial chaos expansion,
multi-rotor vehicle, successive convexification

Authors

NOVÁK, J.; CHUDÝ, P.

RIV year

2024

Released

28.10.2023

Publisher

Institute of Electrical and Electronics Engineers

Location

Barcelona

ISBN

979-8-3503-3357-2

Book

AIAA/IEEE Digital Avionics Systems Conference - Proceedings

ISBN

2155-7195

Periodical

IEEE/AIAA ... Digital Avionics Systems Conference

Number

10

State

United States of America

Pages from

1

Pages to

7

Pages count

7

URL

https://ieeexplore.ieee.org/document/10311265

BibTex

@inproceedings{BUT185182,
  author="Jiří {Novák} and Peter {Chudý}",
  title="Surrogate Modeling of Optimal Control Based Collision Avoidance System for Multirotor Unmanned Aerial Vehicles",
  booktitle="AIAA/IEEE Digital Avionics Systems Conference - Proceedings",
  year="2023",
  journal="IEEE/AIAA ... Digital Avionics Systems Conference",
  number="10",
  pages="1--7",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Barcelona",
  doi="10.1109/DASC58513.2023.10311265",
  isbn="979-8-3503-3357-2",
  issn="2155-7195",
  url="https://ieeexplore.ieee.org/document/10311265"
}