Real-time Explicit Model Predictive Control of Moving Objects Based on Multi-Period Sensor Fusion
(2018.04 ~ 2018.11, ETRI)
For autonomous vehicles, various sensors such as a camera and a LiDAR are used to accurately measure the position of a vehicle. In order to secure the stability of the vehicle, data fusion technology of multiple sensors is an important research field for autonomous vehicles.
The algorithm we proposed in this project designs a state estimator that guarantees stability using a new Lyapunov function for systems with multiple sampling periods.
In addition, the nonlinear system is represented as a fuzzy system and the algorithm is using affine transformed membership functions so that system stability is guaranteed for longer sampling times than conventional methods.
Also, Real time Model Predictive Control technique is applied to improve the control performance of autonomous vehicles.
Development of robust predictive visual servoing method
with fault tolerance (2016.11 ~ 2019.10, NRF)
Visual servo control refers to the use of computer vision data to control the motion of a robot. The vision data may be acquired from a camera that is mounted directly on a robot manipulator or on a mobile robot, in which case motion of the robot induces camera motion, or the camera can be fixed in the workspace so that it can observe the robot motion from a stationary configuration. Other configurations can be considered such as, for instance, several cameras mounted on pan-tilt heads observing the robot motion.