Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

Abstract

In this study, an efficient navigation control method of mobile robot is proposed. The proposed navigation control method consists of behavior manager, toward goal behavior, and wall-following behavior. According to the relative position between the mobile robot and the environment, the behavior manager switches to determine toward goal behavior or wall-following behavior of mobile robot. A novel recurrent fuzzy cerebellar model articulation controller based on an improved dynamic artificial bee colony is proposed for performing wall-following control of mobile robot. The proposed improved dynamic artificial bee colony algorithm uses the sharing mechanism and the dynamic identity update to improve the performance of optimization. A reinforcement learning method is adopted to train the wall-following control of mobile robot. Experimental results show that the proposed method obtains a better navigation control than other methods in unknown environment.

Obstacle avoidance behavior of an autonomous mobile robot in a radioactive environment based on fuzzy logic

“…The platform is assembled with 500 tick motors encoders, 19 cm wheels, robust aluminum body, 8 front faces ultrasonic sensors (sonar), 8 optional real steerable sonars, 1, 2 or 3 hot-swappable batteries and a complete software development kit. The Pioneer 3-DX base platform can reach speeds of 1.6 meters per second and carry a payload of up to 23 kg [11], [12].…”

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

“…This method can be used to analyze mechanisms in system methods. The potential energy and kinetic of each movable part are derived as follows: (6) where θ 0 and θ 2 are two independent variables, which are generalized coordinates. Moreover, two dynamic equations are derived using the Lagrange equation as follows:…”

“…Therefore, several algorithms were used to adjust parameters in neural networks and to find the appropriate network structure. Recently, several researchers [4][5][6][7][8][9] have used various evolutionary algorithms to determine the parameters of neural networks. In [4][5][6][7], several researchers have adopted genetic algorithms (GAs), artificial bee colony (ABC), and gray wolf optimization (GWO) to adjust parameters of neural networks.…”

Nonlinear system control using a fuzzy cerebellar model articulation controller involving reinforcement-strategy-based bacterial foraging optimization

“…The GA adopted in Juang et al, 21 which is based on traditional symbiotic evolution, complements the local mapping property of a fuzzy rule when applied to fuzzy controller design. Li et al 22 proposed an efficient navigation control method of mobile robot. According to the relative position between the mobile robot and the environment, the behavior manager switches to determine toward goal behavior or wall-following behavior of mobile robot.…”