Demo Data from EDBT 2026 / reviewers in the wild / expert
Dimitris Theodorakatos
dblp:72/629
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 |
Multi-agent systems · 56% Motion planning and robot control · 44% |
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 |
0.1 | 1 | 2006 | Totally Distributed Motion Control of Sphere World Multi-agent Systems using Decentralized Navigation Functions · ICRA 2006 |
Knowledge, reasoning and agents › Multi-agent systems › multi-agent control
distributed motion control |
0.1 | 1 | 2006 | Totally Distributed Motion Control of Sphere World Multi-agent Systems using Decentralized Navigation Functions · ICRA 2006 |
Knowledge, reasoning and agents › Multi-agent systems
large-scale multi-agent systems |
0.0 | 1 | 2006 | Totally Distributed Motion Control of Sphere World Multi-agent Systems using Decentralized Navigation Functions · ICRA 2006 |
Methods — techniques the papers use, named apart from their topics
lyapunov stability for hybrid systems · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Totally Distributed Motion Control of Sphere World Multi-agent Systems using Decentralized Navigation FunctionsabstractA distributed feedback control architecture that guarantees collision avoidance and destination convergence for multiple sphere world holonomic agents is presented. The well established tool of decentralized navigation functions is redefined to cope with the communication restrictions of the system. Each agent plans its actions without knowing the destinations of the others and the positions of those agents lying outside its sensing neighborhood. The stability properties of the closed loop system are checked via Lyapunov stability techniques for hybrid systems. The collision avoidance and goal convergence properties are verified through simulations. The key advantage of the proposed algorithm with respect to the previous ones is the significant decrease of computational load and its applicability to large scale groups Dimos V. Dimarogonas, Kostas J. Kyriakopoulos, Dimitris Theodorakatos |
ICRA | 3 |