Adaptive Fuzzy Cognitive Maps Using Interactive Evolution: A Robust Solution for Navigation of Robots

Fuzzy cognitive maps belong to emerging approaches used for various tasks in artificial intelligence. They are especially useful for solving the problem of navigation of vehicles as fuzzy systems are very robust in general. Therefore, they are suitable for the real world applications. One of disadvantages of fuzzy systems is their inability to learn. In this paper, we propose the use of fuzzy cognitive maps for navigation of a humanoid robot Nao and also an adaptive mechanism based on interactive evolution. To get data about the surrounding world, we are using the robot’s camera. Depending on the situation in the arena, the best direction is selected with the use of membership functions for target and obstacles. Parameters of these functions can be set manually from a program interface or the optimal parameters can be found using interactive evolution. The interactive evolution was selected to obtain the best results in the shortest time. Two approaches to the interactive evolution were tested. The first type was a simple interactive evolution, the second type used thresholds to find the most promising individuals to hold the ideal parameters and only these were presented to a human for evaluation. Experiments were made using manual setting of the parameters as well as using the adaptation mechanism of the first and the second type, where the second type was able to find the right set of parameters in a shorter time than the first one.