Ricardo Urvina
Papers
3
Total Citations
30
H-Index
3
About
Ricardo Urvina is an emerging researcher specializing in autonomous mobile robotics, motion planning, and intelligent systems for challenging real-world environments. His work focuses on developing sophisticated navigation strategies for ground robots operating in complex, unstructured terrains — with particular emphasis on agricultural and mining applications. Urvina's most notable contribution is an integrated route and path planning framework for Skid-Steer Mobile Robots (SSMRs) deployed in assisted harvesting tasks. This system elegantly combines global route optimization — grounded in the Traveling Salesman Problem — with terrain traversability analysis, enabling robots to efficiently navigate expansive crop rows under demanding field conditions. The paper has garnered over 16 citations since its 2024 publication, reflecting strong early interest from the robotics and precision agriculture communities. Beyond agriculture, Urvina has extended his expertise to open-pit mining environments, proposing a hybrid path-planning strategy that merges Q-Learning reinforcement learning with RRT* sampling-based techniques. This innovative fusion addresses the challenges of dynamic, hazardous terrains where traditional planners fall short, earning 4 citations shortly after publication. Though early in his career, Urvina's cross-domain contributions signal a promising trajectory, positioning him as a valuable voice in the growing field of field robotics and autonomous systems for industry-critical applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3