Autonomous Drone Racing: A Survey

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00375192" target="_blank" >RIV/68407700:21230/24:00375192 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TRO.2024.3400838" target="_blank" >https://doi.org/10.1109/TRO.2024.3400838</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TRO.2024.3400838" target="_blank" >10.1109/TRO.2024.3400838</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Autonomous Drone Racing: A Survey

Popis výsledku v původním jazyce

Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms that can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground, increasing productivity and further strengthening their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g, respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.

Název v anglickém jazyce

Autonomous Drone Racing: A Survey

Popis výsledku anglicky

Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms that can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground, increasing productivity and further strengthening their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g, respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GM23-06162M" target="_blank" >GM23-06162M: TOPFLIGHT: Plánování trajektorií a misí agilních vzdušných robotů v prostředí s překážkami</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Robotics

ISSN

1552-3098

e-ISSN

1941-0468

Svazek periodika

40

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

3044-3067

Kód UT WoS článku

001241567100001

EID výsledku v databázi Scopus

2-s2.0-85193258792