Simultaneous learning of state-to-state minimum-time planning and control

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00387208" target="_blank" >RIV/68407700:21230/25:00387208 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0013716800003982" target="_blank" >https://doi.org/10.5220/0013716800003982</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0013716800003982" target="_blank" >10.5220/0013716800003982</a>

Result language
angličtina
Original language name
Simultaneous learning of state-to-state minimum-time planning and control
Original language description
This paper tackles the challenge of learning a generalizable minimum-time flight policy for UAVs, capable of navigating between arbitrary start and goal states while balancing agile flight and stable hovering. Traditional approaches, particularly in autonomous drone racing, achieve impressive speeds and agility but are constrained to predefined track layouts, limiting real-world applicability. To address this, we propose a reinforcement learning-based framework that simultaneously learns state-to-state minimum-time planning and control and generalizes to arbitrary state-to-state flights. Our approach leverages Point Mass Model (PMM) trajectories as proxy rewards to approximate the true optimal flight objective and employs curriculum learning to scale the training process efficiently and to achieve generalization. We validate our method through simulation experiments, comparing it against Nonlinear Model Predictive Control (NMPC) tracking PMM-generated trajectories and conducting abla tion studies to assess the impact of curriculum learning. Finally, real-world experiments confirm the robustness of our learned policy in outdoor environments, demonstrating its ability to generalize and operate on a small ARM-based single-board computer.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2
ISBN
978-989-758-770-2
ISSN
2184-2809
e-ISSN
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Number of pages
9
Pages from-to
283-291
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Marbella
Event date
Oct 20, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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