Laviniu Tepelea
Papers
4
Total Citations
19
H-Index
3
About
Laviniu Tepelea is a researcher whose work lies at the intersection of mobile robotics, sensor-based navigation, and image processing. His primary research areas include autonomous robot motion planning, obstacle avoidance, and the application of Cellular Neural Networks (CNNs) to real-time visual guidance systems. Tepelea’s major contributions center on developing efficient, low-cost navigation methods for mobile robots. His most cited work, “Wall-following method for an autonomous mobile robot using two IR sensors” (2008, 8 citations), introduced a simple yet effective local navigation technique relying on just two infrared sensors, demonstrating that robust path-following can be achieved with minimal hardware. He further advanced the field by pioneering the use of CNNs for image-based path planning, as seen in his 2006 paper on motion planning for two robots (5 citations) and his 2011 work on CNN processing techniques for visual guidance (3 citations). His 2015 paper on path planning using intermediate targets (3 citations) addressed the challenge of long-distance navigation by breaking trajectories into manageable segments. Tepelea’s work is notable for bridging theoretical CNN algorithms with practical robotic applications, offering scalable solutions for autonomous navigation in cluttered environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4