Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.

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Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution

Advances in Mechanical Engineering ◽

10.1177/1687814017752483 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401775248 ◽

Cited By ~ 3

Author(s):

Tzu-Chao Lin ◽

Chao-Chun Chen ◽

Cheng-Jian Lin

Keyword(s):

Mobile Robot ◽

Differential Evolution ◽

Fuzzy Controller ◽

Dynamic Group ◽

Unknown Environments ◽

Navigation Control ◽

Interval Type ◽

Wall Following ◽

Neural Fuzzy

This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments.

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Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

Informatics and Education ◽

10.32517/0234-0453-2020-35-1-59-67 ◽

2020 ◽

Vol 1 (1) ◽

pp. 59-67

Author(s):

P. Pei ◽

Yu. N. Petrenko

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Robot Navigation ◽

Mobile Robot Navigation ◽

Industrial Internet Of Things ◽

Navigation Problem ◽

Automatic Navigation ◽

Industrial Internet ◽

Fuzzy Neural ◽

Navigation Control

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

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