O Ugbomoiko Daniel
Papers
1
Total Citations
4
H-Index
1
About
O. Ugbomoiko Daniel is a robotics researcher whose work focuses on autonomous navigation, obstacle detection, and the practical implementation of control algorithms for mobile robots. His most cited paper, "Comparative Analysis of Three Obstacle Detection and Avoidance Algorithms for a Compact Differential Drive Robot in V-Rep" (2019), systematically evaluates and contrasts different sensor-driven approaches for real-time obstacle avoidance in simulated environments. This work provides a valuable benchmark for selecting efficient algorithms in compact, differential-drive platforms, contributing to the broader goal of making autonomous robots more reliable in cluttered spaces. While his citation count is still growing, Daniel’s research is notable for its hands-on, simulation-to-reality methodology—bridging theoretical algorithm design with practical robotic testing. His contributions are particularly relevant for students and engineers developing low-cost, agile robots for indoor navigation tasks. As the field of mobile robotics expands, Daniel’s comparative studies offer a clear, reproducible framework for algorithm selection, helping to accelerate progress in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1