Papers
1
Total Citations
5
H-Index
1
About
K. G. Tucker is a researcher at the forefront of intelligent robotics and autonomous navigation systems. Their primary research areas include deep learning-based object detection, 3D spatial perception, and collision avoidance for indoor robotic platforms. Tucker’s most notable contribution is the development of an integrated framework that combines YOLOv5-based real-time object detection with 3D-depth camera data for robust collision avoidance in indoor environments. This work, published in 2025 and already garnering 5 citations, demonstrates a novel fusion of vision-based distance estimation and rule-based decision-making, enabling robots to navigate complex indoor spaces with enhanced spatial awareness and safety. The system’s practical significance lies in its ability to accurately detect obstacles and estimate distances in real time, addressing critical challenges in autonomous mobile robotics. Tucker’s research bridges the gap between deep learning perception and practical robotic control, offering scalable solutions for applications in service robotics, warehouse automation, and assistive technologies. With a focus on real-world deployment, Tucker’s work continues to influence the development of safer, more intelligent autonomous systems.
Research Focus
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Top Papers
- 1