Papers
17
Total Citations
108
H-Index
5
About
Fidel Aznar is a leading researcher in swarm robotics, with a focus on designing intelligent, decentralized systems for autonomous vehicles. His work primarily explores the intersection of swarm intelligence, stigmergy—a mechanism of indirect coordination—and deep reinforcement learning to create robust, scalable robotic behaviors. Aznar’s most impactful contribution is his pioneering work on modelling oil-spill detection with swarm drones (31 citations), which demonstrated how large groups of simple agents can effectively tackle complex environmental monitoring tasks. He has also advanced the field by developing virtual pheromone-based deployment strategies for multi-rotor UAVs, enabling fault-tolerant navigation in unstructured environments. A key achievement is his integration of deep reinforcement learning to learn swarm foraging behaviors, allowing microscopic fuzzy controllers to direct agents toward collective goals without specialized gripping mechanisms. His recent work on autonomous swarm navigation using multi-agent reinforcement learning and neuro-evolution (2024) continues to push boundaries, addressing the challenge of designing individual behaviors that lead to desired collective outcomes. With a career spanning from L-system-driven self-assembly to probabilistic feature learning for SLAM, Aznar’s research consistently emphasizes practical, realistic deployment approaches, making him a significant contributor to the evolution of autonomous swarm systems.
Research Focus
Key Achievements
Top Papers
- 1Modelling Oil-Spill Detection with Swarm Drones31 citations · 2014
- 2Modelling multi-rotor UAVs swarm deployment using virtual pheromones16 citations · 2018
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- 5L-System-Driven Self-assembly for Swarm Robotics7 citations · 2011
- 6Agents for Swarm Robotics: Architecture and Implementation5 citations · 2011
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- 8Using Gaussian Processes in Bayesian Robot Programming4 citations · 2009
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