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

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

Electronics ◽

10.3390/electronics10040466 ◽

2021 ◽

Vol 10 (4) ◽

pp. 466

Author(s):

Cheng-Hung Chen ◽

Cheng-Jian Lin ◽

Shiou-Yun Jeng ◽

Hsueh-Yi Lin ◽

Cheng-Yi Yu

Keyword(s):

Mobile Robot ◽

Differential Evolution ◽

Obstacle Avoidance ◽

Fuzzy Controller ◽

Robot Navigation ◽

Mobile Robot Navigation ◽

Knowledge Based ◽

Automatic Navigation ◽

Navigation Control ◽

Neural Fuzzy

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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Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

Mathematics ◽

10.3390/math8081223 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1223

Author(s):

Cheng-Hung Chen ◽

Shiou-Yun Jeng ◽

Cheng-Jian Lin

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Fuzzy Neural Network ◽

Artificial Bee Colony ◽

Mobile Robot Navigation ◽

Adaptive Fuzzy Neural Network ◽

Adaptive Fuzzy ◽

Bee Colony ◽

Fuzzy Neural ◽

Navigation Control

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

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