We integrate control systems and computer vision along with machine learning in the interdisciplinary field of robotics and focuses on visual servo control, robot vision, unmanned aerial vehicles (UAVs), cooperative control of multiple autonomous robots, and fusion of visual and force feedback in robotic systems. Control systems are ubiquitous in daily life. Complex physical systems such as robots, cars, wind turbines, UAVs and microsystems all require robust and high performance controllers. With the increased use of machine learning in computer vision, it is now possible to find fast and accurate solutions to many practical tasks including object detection and tracking, object recognition and localization.

We develop real-time control and vision systems for civilian and military applications. Research efforts are directed toward modelling and simulation of virtually all kinds of dynamical systems such as UAVs, wind turbines and automotive systems, and their control through robust, high performance controllers, and design of machine vision systems for real-time monitoring and quality control inspection of various industrial products and processes.

• Novel design of experiment (DoE) procedures for identification of physical systems

• Real-time control algorithms and software for industrial applications

• Real-time vision algorithms and software for civilian and military applications

• Design and development of hybrid UAVs

We provide solutions for automotive, aerospace, agriculture, biomedical and energy industries.

• 2D and 3D Cameras

• FLIR Thermal Camera

• Digital Image Correlation (DIC) System

• NI Data Acquisition (DAQ) System

• Workstations

• Quadrotor type UAVs

• Tilt-wing UAVs

• Mobile robots

• IMU sensors

• Indoor positioning system