Fuzzy Social Force Model for Robot Soccer Navigation: A Preliminary Report

This paper proposes an adaptive control strategy to improve the performance of the Social Force Model (SFM) based mobile soccer robot navigation using the Fuzzy Inference System (FIS). The combination of FIS dan SFM is then called a Fuzzy Social Force Model (FSFM). In the FSFM strategy, FIS is used to adaptively adjust the parameters of SFM based on the stimulus from the external condition, namely the obstacle’s relative distance to the robot, d, and its direction, γ, so that the robot’s reactivity and responsiveness can be automatically adjusted. Currently, we adjust only one parameter, namely obstacle’s gain value, k. Obstacle’s gain value will control the amount of forces produced by the closest obstacle. We tested our proposed method using a realistic 3D simulator, called V-Rep, by utilizing an omnidirectional robot model from Festo, namely Robotino. Our experimental results show that our proposed FSFM can work well by always successfully finishing all of the trials with less collision with obstacles. The comparison results with the fixed-parameter method prove that our proposed method is better and very promising to be implemented for real robot applications.