Christopher Nenebi

North Carolina Agricultural and Technical State University

Papers

Total Citations

H-Index

About

Christopher Nenebi is a researcher whose work sits at the intersection of robotics, computer vision, and intelligent navigation systems. His primary research focuses on developing robust frameworks for autonomous indoor navigation, with a particular emphasis on collision avoidance through the integration of deep learning and 3D spatial awareness. His most cited work, "Integrating Deep Planning-Based Object Detection with 3D-Depth Camera for Collision Avoidance in Indoor Robotics Navigation" (2025), introduces a novel system that combines YOLOv5-based real-time object detection with depth camera distance estimation and rule-based decision-making. This framework enables robots to achieve accurate spatial awareness and navigate complex indoor environments safely. By bridging the gap between vision-based perception and practical robotic control, Nenebi's contributions have already garnered 5 citations in a short time, signaling growing impact in the field. His work is particularly valuable for applications in service robotics, warehouse automation, and assistive technologies, where reliable collision avoidance is critical. Nenebi continues to advance the state of the art in intelligent robotic systems, making his research essential reading for students and engineers working on autonomous navigation and human-robot interaction.