Fuzzy based obstacle avoidance for mobil robots with Kinect sensor

This article describes an improved version of a fuzzy-based navigational system and compares the developed system with the Caversan [11] navigational method that was used as a basis of our approach. Our system (which uses a Mamdani-type fuzzy controller that has sixteen rules, three inputs and one output) has better algorithms for both the path planning and for the avoiding of obstacles. This is because our approach takes into consideration both the point-like obstacles in the front, the close walls, and the obstacles on the left as well; these are the aspects where the reference system makes a false assumption and the robot continues going straight forward. Our new system does a sharp turn if it detects an obstacle that is close, and goes on an almost ideal track if the obstacles are further away. The developed system was tested both in the MATLAB fuzzy simulator and using an autonomously moving robotic car as well; and it was determined that the robot is capable of navigation in real time while it avoids the obstacles. Since the evaluation of the environment is done using Kinect and infra-based distance-detection sensors, we introduced a new fuzzyfication method. Because of this, the parameters of the original method had to be adjusted to match the sensor's field of view and area of detection (this had to be done separately for the distances on the sides and in the front as well).