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.
Highlights
In recent years, mobile robot has become one of the popular researches
This study proposes a recurrent fuzzy cerebellar model articulation controller (RFCMAC) based on the improved dynamic artificial bee colony (IDABC) algorithm to achieve mobile robot WF control
A RFCMAC based on an IDABC is proposed for controlling the mobile robot WF in an unknown environment
Summary
Mobile robot has become one of the popular researches. It is widely used in many applications to assist people solving many problems, such as environmental exploration, suspicious objects removal, object handling, and navigation.[1,2,3] In order to achieve these objectives, the popular research topics of the mobile robot control include the obstacle avoidance, wall-following, navigation, and target tracking.Recently, many researchers have successfully used various methods[4,5] on mobile robot control. A multiobjective, rule-coded, advanced, continuous-ant-colony optimization (MO-RACACO) algorithm[14] was proposed for fuzzy controller (FC) design and was applied on multi-objective WF control for a mobile robot. Based on interval type-2 fuzzy controller (IT2FC), Hsu and Juang proposed an evolutionary WF control for mobile robot and defined a cost function to assess the WF performance of an evolutionary IT2FC.[15] Contreras-Cruz et al.[16] combined the artificial bee colony (ABC) algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures for solving the mobile robot path planning problem.
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