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, and hardware subsystems.
Required Qualifications
Master’s or PhD in Robotics, Control Systems, or a related field.
3+ years of experience in robotics control system development or equivalent R&D experience (PhD candidates with strong resea...