Abstract
In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed group-based strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wall-following control than other methods in unknown environments.
Highlights
In recent years, mobile robot control is one of the popular research areas, which includes wall-following control, navigation, parallel parking and path tracking, and so on
The proposed group-based strategy bacterial foraging optimization (GSBFO) has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate
In order to verify the performance of proposed GSBFO, we will apply the mobile robot wall-fallowing control problem and compare the proposed method with various methods
Summary
Mobile robot control is one of the popular research areas, which includes wall-following control, navigation, parallel parking and path tracking, and so on. An FCMAC model is proposed for mobile robot wall-following control. Experimental results show that the proposed FCMAC with GSBFO can successfully implement mobile robot wall-following control and has a better performance than other methods.
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