·       Design and implement advanced control algorithms (e.g., PID, adaptive control, model predictive control) tailored for autonomous robotic platforms.

·       Optimize motion planning and path control strategies to enhance multi-robot coordination and operational efficiency.

·       Develop real-time control architectures that leverage edge computing for low-latency decision-making.

·       Integrate adaptive learning techniques to continuously refine control performance, particularly for dynamic systems.

·       Develop and test control algorithms in both simulation and real-world experiments, utilizing aerial simulation tools (e.g., PX4 SITL, AirSim) when applicable.

·       Collaborate with interdisciplinary teams to integrate control systems with AI, sensor fusion, an...