Jonathan M. Garibaldi

University of Nottingham

Papers

17

Total Citations

522

H-Index

11

About

Jonathan M. Garibaldi is a researcher whose work spans autonomous robotics, artificial immune systems, and fuzzy logic control — fields at the intersection of bio-inspired computation and intelligent systems. His most recognized contribution, a potential field-based approach to multi-robot search and rescue (2007, 151 citations), established foundational techniques for coordinating robot swarms in complex environments. Alongside this, Garibaldi pioneered the application of artificial immune system models to mobile robotics, drawing on Jerne's idiotypic network theory to develop adaptive, behavior-based controllers — work reflected across several influential papers from 2007 to 2009. He has also made significant strides in fuzzy logic control, exploring the advantages of Type-2 fuzzy systems over classical Type-1 approaches in micro-robot and UAV navigation contexts, including a notable 2018 study (65 citations) addressing input uncertainty in quadrotor control using nonsingleton fuzzy logic. His more recent research extends these principles to wearable robotic systems integrating origami structures and Type-2 fuzzy decision-making. Collectively, Garibaldi's body of work demonstrates a sustained commitment to making autonomous and adaptive robotic systems more robust, intelligent, and practically deployable.

Research Focus

Key Achievements

11
H-Index
17
Papers
522
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
Multi-Robot Search and Rescue: A Potential Field Based Approach
151 citations · 2007
📈 Most Prolific Year: 2009 (4 Papers)
🤝 Key Collaborators: 22
🏛 Institutions: University of Nottingham

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago