Daniel P. Brogan
Papers
1
Total Citations
45
H-Index
1
About
Daniel P. Brogan is a leading researcher at the intersection of deep learning, computer vision, and robotic automation, with a primary focus on enabling intelligent disassembly and servicing systems. His most-cited work (45 citations) tackles the critical challenge of fastener detection for robotic applications, demonstrating how deep learning models can be optimized through careful tuning of input resolution and network architecture to achieve robust generalization across diverse visual environments. This foundational contribution addresses a key bottleneck in automated maintenance and recycling processes. Brogan’s research has significant implications for circular economy initiatives, where reliable vision-guided robotics are essential for dismantling complex products. By advancing the practical deployment of convolutional neural networks in industrial settings, his work bridges the gap between state-of-the-art AI and real-world manufacturing challenges. His findings are particularly valued by researchers and engineers developing autonomous systems for electronic waste processing and aerospace servicing, where precision and adaptability are paramount. Through his methodical approach to model optimization, Brogan continues to shape the future of intelligent robotic interaction with unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1