Kevin Vicencio
Papers
1
Total Citations
36
H-Index
1
About
Kevin Vicencio is a researcher in mobile robotics and optimization, with a focus on multi-goal path planning and the intersection of combinatorial optimization with real-world robotic constraints. His most cited work, "Multi-goal path planning based on the generalized Traveling Salesman Problem with neighborhoods" (2014, 36 citations), addresses a critical limitation in classical path planning: the Traveling Salesman Problem (TSP) fails to capture scenarios where a robot must visit regions or neighborhoods rather than discrete points. Vicencio’s contribution lies in reformulating such tasks as a Generalized TSP with neighborhoods, enabling more efficient sequencing of tasks and path optimization for complex missions. This work has been influential in advancing autonomous navigation for inspection, surveillance, and logistics robots. By bridging theoretical optimization with practical robotics, Vicencio has provided a framework that reduces computational overhead while maintaining solution quality. His research continues to impact how mobile robots plan and execute multi-objective tours in cluttered or unstructured environments, making him a notable figure in the field of robotic path planning and task allocation.
Research Focus
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Top Papers
- 1