Ioannis Daramouskas
Papers
1
Total Citations
4
H-Index
1
About
Ioannis Daramouskas is a researcher whose work lies at the intersection of autonomous systems and machine learning, with a particular focus on unmanned aerial vehicles (UAVs). His most notable contribution is a pioneering methodology that enables drones to learn autonomous navigation and obstacle avoidance using decision trees, a computationally efficient approach that balances real-time performance with interpretability. Published in 2020, this work has garnered 4 citations, establishing a foundation for lightweight, rule-based learning in UAV control systems. Daramouskas’s research addresses a critical challenge in robotics: enabling drones to operate safely in dynamic, real-world environments without relying on heavy computational resources. His approach is particularly valuable for applications in search-and-rescue, environmental monitoring, and infrastructure inspection, where reliable, low-latency decision-making is essential. By demonstrating that decision trees can effectively guide drones through complex spaces, Daramouskas has contributed to making autonomous navigation more accessible and practical. His work continues to influence researchers exploring the balance between algorithmic simplicity and robust performance in aerial robotics.
Research Focus
Key Achievements
Top Papers
- 1