The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network.

Ganlin Wang,Yufei Li,Lanfei Zhao,Xiaosong Fan

doi:10.3389/fnbot.2021.634340

Abstract

The trajectory tracking and control of incomplete mobile robots are explored to improve the accuracy of the trajectory tracking of the robot controller. First, the mathematical kinematics model of the non-holonomic mobile robot is studied. Then, the improved Backpropagation Neural Network (BPNN) is applied to the robot controller. On this basis, a mobile robot trajectory tracking controller combining the fuzzy algorithm and the neural network is designed to control the linear velocity and angular velocity of the mobile robot. Finally, the robot target image can be analyzed effectively based on the Internet of Things (IoT) image enhancement technology. In the MATLAB environment, the performances of traditional BPNN and improved BPNN in mobile robots' trajectory tracking are compared. The tracking accuracy before and after the improvement shows no apparent differences; however, the training speed of improved BPNN is significantly accelerated. The fuzzy-BPNN controller presents significant improvements in tracking speed and tracking accuracy compared with the improved BPNN. The trajectory tracking controller of the mobile robot is designed and improved based on the fuzzy BPNN. The designed controller combining the fuzzy algorithm and the improved BPNN can provide higher accuracy and tracking efficiency for the trajectory tracking and control of the non-holonomic mobile robots.

Highlights

As human society enters the era of science and technology, computers, and artificial intelligence have developed rapidly; machines to replace human labor to improve production efficiency have become a reality (Ma et al, 2020)
The effects of traditional Backpropagation Neural Network (BPNN) and improved BPNN in mobile robots’ trajectory tracking are compared, and the results are shown in Figures 6 and 7
The improved BPNN algorithm has a slight improvement in tracking accuracy than the traditional BPNN; the difference between the two is not notable

Summary

Introduction

As human society enters the era of science and technology, computers, and artificial intelligence have developed rapidly; machines to replace human labor to improve production efficiency have become a reality (Ma et al, 2020). Internet of Things (IoT) is a famous object vision. Connecting a simple robot to the internet will become valuable because it can obtain updated information about its environment from sensors or understand the user’s whereabouts and the status of nearby devices (Marques et al, 2019). The core of “IoT+Robot” is the ubiquitous sensors, cameras, and actuators embedded in the environment, as well as autonomous robots that collect data in real-time (Rehman et al, 2019). Connecting machine vision systems to IoT can create powerful network functions that can recognize objects from cameras. Such functions can enhance the local nodes’ intelligence and autonomy, reducing the processing load on the central server and achieving a better-distributed control architecture

Methods

Results

Conclusion