Papers

3

Total Citations

13

H-Index

2

About

D. B. J. Bussey’s research bridges two seemingly distinct frontiers: artificial intelligence for robotics and the infrastructure for deep-space exploration. In the field of robotics, Bussey pioneered the application of convolutional neural network (CNN) transfer learning for robust face recognition in NAO humanoid robots, demonstrating that pre-trained CNN architectures could be efficiently adapted for real-time human-robot interaction. This work, which has garnered 8 citations, offered a practical alternative to training neural networks from scratch, advancing the deployment of social robots. Simultaneously, Bussey contributed to the foundational architecture of lunar exploration, authoring key studies on scalable small-spacecraft navigation and communication networks for the Moon. These papers, cited 3 and 2 times respectively, proposed evolvable infrastructure designs that supported the NASA Exploration Initiative’s vision of sustained human and robotic lunar missions. By addressing both the cognitive capabilities of autonomous systems and the physical communication backbones needed for off-world operations, Bussey’s work has had a dual impact—enabling smarter robots on Earth and smarter mission planning for the Moon and beyond.

Research Focus

Key Achievements

2
H-Index
3
Papers
13
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Convolutional neural network transfer learning for robust face recognition in NAO humanoid robot
8 citations · 2017
📈 Most Prolific Year: 2017 (1 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: Embry–Riddle Aeronautical University, Johns Hopkins University Applied Physics Laboratory

Top Papers

  1. 1
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  3. 3

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago