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NOVÁK, J. CHUDÝ, P.
Original Title
Surrogate Modeling of Optimal Control Based Collision Avoidance System for Multirotor Unmanned Aerial Vehicles
Type
conference paper
Language
English
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.
Keywords
collision avoidance, polynomial chaos expansion, multi-rotor vehicle, successive convexification
Authors
NOVÁK, J.; CHUDÝ, P.
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
2155-7195
Number
10
Pages from
1
Pages to
7
Pages count
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", 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" }