EDBT 2026 Demo / reviewers in the wild / expert
Pascal Stoop
dblp:359/1181
· DBLP profile ↗
1ranked-venue papers
1as 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 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 · 87% Efficient and distributed learning · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
collision avoidance |
0.8 | 1 | 2024 | A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2 · ICRA 2024 |
Robotics › Motion planning and robot control
motion planning |
0.8 | 1 | 2024 | A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2 · ICRA 2024 |
Machine learning › Efficient and distributed learning
asynchronous execution |
0.2 | 1 | 2024 | A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2 · ICRA 2024 |
Methods — techniques the papers use, named apart from their topics
distributed execution · 0.8centralized scheduling · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2abstractThis paper introduces a method that enables the parallel independent execution of trajectories for multi-robot / multi-arm systems in a shared workspace in MoveIt2. The proposed method leverages a centralized scheduler in a distributed set up to prevent collisions while the robots move independently. We argue that this approach is better suited than the state of the art (i.e., synchronous execution) for flexible/adaptive robotic tasks where the actions to be performed may vary in planning and execution time depending on sensor data (e.g., pick and place with inspection, assembly) as it is able to reduce the total execution time w.r.t. current approaches leveraging a single arm or multiple arms with synchronous motion planning. Pascal Stoop, Tharaka Ratnayake, Giovanni Toffetti Carughi |
ICRA | 1 |