Demo Data from EDBT 2026 / reviewers in the wild / expert
Vishnu Veeraraghavan
dblp:371/8513
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0009-7893-2741ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › path planning
coverage path planning |
0.8 | 1 | 2024 | Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure · IEEE Trans. Robotics 2024 |
Smart cities and intelligent transportation
infrastructure inspection |
0.2 | 1 | 2024 | Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure · IEEE Trans. Robotics 2024 |
Methods — techniques the papers use, named apart from their topics
online planning · 1.5nozzle motion control · 1.5cell decomposition · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil InfrastructureabstractWe present a simultaneous sensor-based inspection and footprint coverage (SIFC) planning and control design with applications to autonomous robotic crack mapping and filling. The main challenge of the SIFC problem lies in the coupling of complete sensing (for mapping) and robotic footprint (for filling) coverage tasks. Initially, we assume known target information (e.g., cracks) and employ classic cell decomposition methods to achieve complete sensing coverage of the workspace and complete robotic footprint coverage using the least-cost route. Subsequently, we generalize the algorithm to handle unknown target information, allowing the robot to scan and incrementally construct the target map online while conducting robotic footprint coverage. The online polynomial-time SIFC planning algorithm minimizes the total robot traveling distance, guarantees complete sensing coverage of the entire workspace, and achieves near-optimal robotic footprint coverage, as demonstrated through empirical experiments. For the demonstrated application, we design coordinated nozzle motion control with the planned robot trajectory to efficiently fill all cracks within the robot's footprint. Experimental results illustrate the algorithm's design, performance, and comparisons. The SIFC algorithm offers a high-efficiency motion planning solution for various robotic applications requiring simultaneous sensing and actuation coverage. Vishnu Veeraraghavan, Kyle Hunte, Jingang Yi, Kaiyan Yu |
IEEE Trans. Robotics | 1 |