Demo Data from EDBT 2026 / reviewers in the wild / expert
Nuraddin Kerimov
dblp:395/6741
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
path planning |
0.9 | 1 | 2025 | Safe Interval Randomized Path Planning For Manipulators · ICAPS 2025 |
Robotics › Motion planning and robot control › path planning › search-based path planning
safe interval path planning |
0.9 | 1 | 2025 | Safe Interval Randomized Path Planning For Manipulators · ICAPS 2025 |
Robotics › Motion planning and robot control › motion planning
sampling-based motion planning |
0.9 | 1 | 2025 | Safe Interval Randomized Path Planning For Manipulators · ICAPS 2025 |
Methods — techniques the papers use, named apart from their topics
heuristic search · 0.9bidirectional RRT-Connect · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Safe Interval Randomized Path Planning For ManipulatorsabstractPlanning safe paths in 3D workspace for high DoF robotic systems, such as manipulators, is a challenging problem, especially when the environment is populated with the dynamic obstacles that need to be avoided. In this case the time dimension should be taken into account that further increases the complexity of planning. To mitigate this issue we suggest to combine safe-interval path planning (a prominent technique in heuristic search) with the randomized planning, specifically, with the bidirectional rapidly-exploring random trees (RRT-Connect) -- a fast and efficient algorithm for high-dimensional planning. Leveraging a dedicated technique of fast computation of the safe intervals, we end up with an efficient planner dubbed SI-RRT. We compare it with the state of the art and show that SI-RRT consistently outperforms the competitors both in runtime and solution cost. Nuraddin Kerimov, Aleksandr Onegin, Konstantin S. Yakovlev |
ICAPS | 1 |