Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Sensors

Chun-Hui Lin¹  Shyh-Hau Wang¹  Cheng-Jian Lin² 

[1] Department of Computer Science & Information Engineering, Nation Cheng Kung University, Tainan 701, Taiwan;Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan;

关键词: evolutionary robot;    navigation control;    fuzzy control;    wall-following control;    cooperative carrying;    interval type-2 neural fuzzy controller;    artificial bee colony algorithm;    grouping strategy;   

DOI  :  10.3390/s18124181

来源: DOAJ

【 摘 要 】

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

【 授权许可】

Unknown