Welcome to the Control and Robotics Lab (CtrlRobot-Lab)
One of our research directions is task sequence and robotic motion planning in a human-robot collaborative environment. We are particularly interested in such developments to improve the efficiency and effectiveness of the disassembly, recycling, and remanufacturing of end-of-use products such as e-wastes. Another research direction is learning-based control to enable learning among heterogeneous robots toward their mass customization and application. We are also interested in collaborative estimation using connected vehicles, optimization and control for power and energy storage systems, as well as iterative learning control for high-precision systems.