Research Interests
Robotics, Multi-Drone Systems, Control Systems, Machine Learning
Tajnimul Hossain focuses on the design and control of intelligent robotic systems, with a particular emphasis on multi-drone coordination and autonomous robotics. His research explores how advanced control strategies and machine learning techniques can be integrated to enable reliable, scalable, and safe operation of multiple robots in real-world environments.
Research Focus
His primary research direction involves formation control, trajectory planning, and cooperative control of unmanned aerial vehicles (UAVs) for applications such as infrastructure inspection, monitoring, and autonomous deployment. By combining classical control theory with data-driven methods, his work aims to enhance robustness, adaptability, and fault tolerance in dynamic and uncertain environments.
Current Interests
He is developing control-aware learning frameworks, where stability principles from control theory—such as feedback control and Lyapunov-based methods—are embedded into learning-based robotic systems. This approach seeks to bridge the gap between theoretical guarantees and real-world deployment of autonomous multi-robot systems.