Marie Schwahn

dblp:383/4605 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Legged, aerial and field robots · 72% Motion planning and robot control · 28%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › aerial robots
autonomous landing
0.812024
Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments · ICRA 2024
Robotics › Motion planning and robot control
robot control
0.812024
Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments · ICRA 2024
Robotics › Legged, aerial and field robots › aerial robots
unmanned aerial vehicle
0.812024
Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments · ICRA 2024
Robotics › Legged, aerial and field robots › field robotics
environmental monitoring
0.212024
Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments · ICRA 2024
Robotics › Legged, aerial and field robots
field robotics
0.212024
Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments · ICRA 2024

Methods — techniques the papers use, named apart from their topics

visual tracking · 0.8pose error correction · 0.8attitude alignment · 0.8
YearPublicationVenuePosition
2024 Autonomous UAV Mission Cycling: A Mobile Hub Approach for Precise Landings and Continuous Operations in Challenging Environments
abstract
Environmental monitoring via UAVs offers unprecedented aerial observation capabilities. However, the limited flight durations of typical multirotors and the demands on human attention in outdoor missions call for more autonomous solutions. Addressing the specific challenges of precise UAV landings – especially amidst wind disturbances, obstacles, and unreliable global localization – we introduce a mobile hub concept. This hub facilitates continuous mission cycling for unmodified off-the-shelf UAVs. Our approach centers on a small landing platform affixed to a robotic arm, adeptly correcting UAV pose errors in windy conditions. Compact enough for installation in an economy car, the system emphasizes two novel strategies. Firstly, external visual tracking of the UAV informs the landing controls for both the drone and the robotic arm. The arm compensates for UAV positioning errors and aligns the platform’s attitude with the UAV for stable landings, even on small platforms under windy conditions. Secondly, the robotic arm can transport the UAV inside the hub, perform maintenance tasks like battery replacements, and then facilitate direct relaunches. Importantly, our design places all operational responsibility on the hub, ensuring the UAV remains unaltered. This ensures broad compatibility with standard UAVs, only necessitating an API for attitude setpoints. Experimental results underscore the efficiency of our model, achieving safe landings with minimal errors (≤ 7 cm) in winds up to 5 Beaufort (8.1 m/s). In essence, our mobile hub concept significantly boosts UAV mission availability, allowing for autonomous operations even under challenging conditions.
Alexander Moortgat-Pick, Marie Schwahn, Anna Adamczyk, Daniel-André Duecker, Sami Haddadin
ICRA2