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Vishnu Veeraraghavan

dblp:371/8513 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › path planning
coverage path planning
0.812024
Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure · IEEE Trans. Robotics 2024
Smart cities and intelligent transportation
infrastructure inspection
0.212024
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
YearPublicationVenuePosition
2024 Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure
abstract
We 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. Robotics1