Daniel McGibney
Papers
1
Total Citations
4
H-Index
1
About
Daniel McGibney is a researcher whose work lies at the intersection of robotics, distributed systems, and multi-sensor perception. His key research areas include cooperative multi-robot systems, object classification, and the integration of audio and visual data for enhanced environmental understanding. McGibney’s major contribution is the development of a cooperative distributed object classification framework that leverages audio features to improve real-time visual tracking. By enabling multiple robots to collaboratively classify objects through sound, his work addresses the challenge of accurately tracking and describing objects of interest in dynamic environments. This approach, demonstrated with four distinct object types, showcases the potential of combining auditory and visual cues for more robust robotic perception. While his most-cited paper, “Cooperative distributed object classification for multiple robots with audio features,” has garnered 4 citations, its impact lies in laying foundational groundwork for multi-modal sensing in robotics. McGibney’s research offers valuable insights for students and researchers exploring sensor fusion, distributed intelligence, and the practical application of audio in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1