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RAVLAB Projects

Autonomous Model Car

On a mechanical structure of a model car, which is used to be controlled by a remote controller, we have installed an infrastructure containing microcontroller cards, several sensors, and a computer. Using artificial intelligence algorithms to detect obstacles around, this car is able to move one point to another point desired without any human interaction. The purpose of the autonomous car racing project, on which undergraduate students are working, is to equip the model car with various artificial intelligence algorithms so that the car can traverse a runway as early as possible.



Autonomous Air Vehicle

In this project, a model quadcopter (four-bladed helicopter) air vehicle has equipped with a computer, some sensors, and microcontroller cards. The system has been designed and realized to fly without human control, using only the information coming from the sensor units and artificial intelligence algorithms processing the information. When the project is completed it can used for many different purposes, such as coastal security, traffic control and surveillance, pipeline observation, air monitoring / surveillance, vehicle and human tracking and fire monitoring, aerial photography, aerial filming.




Ground Plane Detection Using Kinect Sensor

Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a novel and robust ground plane detection algorithm using depth information acquired by a Kinect sensor. Unlike similar methods from the literature, we do not assume that the ground plane covers the largest area in the scene. Furthermore our algorithm handles two different conditions: fixed and changing view angle of the sensor. We show that the algorithm is robust if the view angle is fixed whereas an additional procedure handles different view angles satisfactorily.