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Simón Martinez-Rozas

dblp:254/0409 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-3289-3371ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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
Motion planning and robot control · 50% Legged, aerial and field robots · 50%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › aerial robots › unmanned aerial vehicle
tethered aerial robot
0.512021
Optimization-based Trajectory Planning for Tethered Aerial Robots · ICRA 2021
Robotics › Motion planning and robot control
trajectory planning
0.512021
Optimization-based Trajectory Planning for Tethered Aerial Robots · ICRA 2021

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

nonlinear optimization · 0.5multi-objective optimization · 0.5
YearPublicationVenuePosition
2021 Optimization-based Trajectory Planning for Tethered Aerial Robots
abstract
This paper presents a non-linear optimization method for trajectory planning of tethered aerial robots. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV and tether.
Simón Martinez-Rozas, David Alejo, Fernando Caballero, Luis Merino
ICRA1