EDBT 2026 Demo / reviewers in the wild / expert
Anil Özdemir
dblp:207/4768
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 2 · 1 first-author
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 · 77% Motion planning and robot control · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems
swarm robotics |
0.4 | 1 | 2019 | Spatial Coverage Without Computation · ICRA 2019 |
Robotics › Motion planning and robot control › path planning
maze navigation |
0.1 | 1 | 2019 | Spatial Coverage Without Computation · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
offline optimization · 0.4e-puck robots · 0.4deterministic controller · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Spatial Coverage Without ComputationabstractWe study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage. The solution requires only a single bit of information per robot-whether or not another robot is present in its line of sight. Computer simulations show that our deterministic controller, which was obtained through off-line optimization, achieves around 71-76% coverage in a test scenario with no robot redundancy, which corresponds to a 26-39% reduction of the area that is not covered, when compared to an optimized random walk. A moderately lower level of performance was observed in 20 experimental trials with 25 physical e-puck robots. Moreover, we demonstrate that the same controller can be used in environments of different dimensions and even to navigate a maze. The controller provides a baseline against which one can quantify the performance improvements that more advanced and expensive techniques may offer. Moreover, due to its simplicity, it could potentially be implemented on swarms of sub-millimeter-sized robots. This would pave the way for new applications in micro-medicine. Anil Özdemir, Melvin Gauci, Andreas Kolling, Matthew D. Hall, Roderich Groß |
ICRA | 1 |
| 2019 | Decentralized Pose Control of Modular Reconfigurable Robots Operating in Liquid EnvironmentsabstractModular reconfigurable robots are touted for their flexibility, as their bodies can assume a wide range of shapes. A particular challenge is to make them move efficiently in 3D without compromising the scalability of the system. This paper proposes decentralized and fully reactive controllers for pose control of 3D modular reconfigurable robots. The robots operate in liquid environments, and move by routing fluid through themselves. Each module uses only two bits of sensory information per face. Additionally, the modules can use up to five bits of information that are exchanged via shared power lines. We prove that robots of convex shape are guaranteed to reach a goal object with a preferred orientation. Using computer simulations of Modular Hydraulic Propulsion robots, all controllers are assessed for different environments, system sizes and noise, and their performances compared against a centralized controller. Given the simplicity of the solutions, modules could be realized at scales below a millimeter-cube, where robots of high spatial resolution could perform accurate movements in 3D liquid environments. João V. Amorim Marques, Anil Özdemir, Matthew J. Doyle, Daniela Rus, Roderich Groß |
IROS | 2 |