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Anders Schack Madsen

dblp:353/5889 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—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 · 50% Motion planning and robot control · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Energy systems and smart grids · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.712023
Towards Autonomous UAV Railway DC Line Recharging: Design and Simulation · ICRA 2023
Robotics › Motion planning and robot control
trajectory planning and tracking
0.712023
Towards Autonomous UAV Railway DC Line Recharging: Design and Simulation · ICRA 2023

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

hardware-in-the-loop simulation · 1.3
YearPublicationVenuePosition
2023 Towards Autonomous UAV Railway DC Line Recharging: Design and Simulation
abstract
Autonomously recharging UAVs from existing infrastructure has enormous potential for various applications, such as infrastructure inspection, surveillance, and search and rescue. While it is an active area of research, most related work focuses on alternating current (AC) infrastructure while very little work has been done on investigating the potential of recharging UAVs from direct current (DC) infrastructure. This work proposes a UAV system designed to autonomously recharge from existing DC infrastructure. Two onboard powerline grippers and a motorized cable drum enable the UAV to perform a two-stage landing on railway DC lines where a wire is connected between them through the UAV for recharging. Light-weight electronics designed to be carried by the UAV are developed to harvest energy from up to 3kV DC railway lines. The recharge mission is autonomously executed using fully onboard and real-time perception and trajectory planning and tracking algorithms. The potential of the system is shown in lab setting validation, with hardware-in-the-loop simulation, and partly in a real overhead powerline environment, verifying the functionality of the sub-components.
Frederik Falk Nyboe, Nicolaj Malle, Gerd vom Bögel, Linda Cousin, Thomas Heckel, Konstantin Troidl, Anders Schack Madsen, Emad Samuel Malki Ebeid
ICRA7