Yosuef Alotaibi
Papers
1
Total Citations
5
H-Index
1
About
Yosuef Alotaibi is a rising researcher in robotics and computer vision, whose work centers on enabling autonomous mobile robots to navigate complex, dynamic environments with greater efficiency and safety. His primary contributions lie in developing lightweight, real-time systems that combine monocular depth estimation with behavior-driven control, allowing robots to avoid obstacles without relying on expensive or bulky sensors. His most-cited paper, "Real-time vision-based obstacle avoidance for mobile robots using lightweight monocular depth estimation and behavior-driven control" (2025), has garnered 5 citations in its early stages, signaling growing interest in his practical, computationally efficient approach. This work is notable for its potential to democratize advanced navigation capabilities for smaller, resource-constrained robots, making it relevant to applications in service robotics, autonomous delivery, and exploration. Alotaibi’s research bridges the gap between theoretical computer vision models and real-world robotic deployment, emphasizing robustness and low-latency performance. As his citation count rises, he is establishing himself as a key voice in the quest for affordable, vision-driven autonomy—a field poised to transform how machines interact with the physical world.
Research Focus
Key Achievements
Top Papers
- 1